Overview

Dataset statistics

Number of variables33
Number of observations3082
Missing cells32212
Missing cells (%)31.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory794.7 KiB
Average record size in memory264.0 B

Variable types

Categorical17
Numeric12
Unsupported4

Alerts

image.medium has a high cardinality: 1017 distinct values High cardinality
_embedded.show.url has a high cardinality: 633 distinct values High cardinality
_embedded.show.summary has a high cardinality: 556 distinct values High cardinality
_embedded.show.premiered has a high cardinality: 407 distinct values High cardinality
_embedded.show.officialSite has a high cardinality: 570 distinct values High cardinality
_embedded.show.name has a high cardinality: 631 distinct values High cardinality
_embedded.show.image.original has a high cardinality: 585 distinct values High cardinality
_embedded.show.externals.imdb has a high cardinality: 309 distinct values High cardinality
_embedded.show.ended has a high cardinality: 112 distinct values High cardinality
_embedded.show._links.self.href has a high cardinality: 633 distinct values High cardinality
_embedded.show.id is highly correlated with _embedded.show.type and 13 other fieldsHigh correlation
_embedded.show.runtime is highly correlated with _embedded.show.id and 7 other fieldsHigh correlation
_embedded.show.averageRuntime is highly correlated with _embedded.show.type and 9 other fieldsHigh correlation
_embedded.show.externals.thetvdb is highly correlated with _embedded.show.id and 9 other fieldsHigh correlation
id is highly correlated with _embedded.show.id and 6 other fieldsHigh correlation
_embedded.show.dvdCountry.timezone is highly correlated with df_index and 13 other fieldsHigh correlation
_embedded.show.schedule.time is highly correlated with df_index and 17 other fieldsHigh correlation
_embedded.show.language is highly correlated with df_index and 17 other fieldsHigh correlation
_embedded.show.status is highly correlated with _embedded.show.type and 9 other fieldsHigh correlation
_embedded.show.dvdCountry.name is highly correlated with df_index and 13 other fieldsHigh correlation
_embedded.show.dvdCountry.code is highly correlated with df_index and 13 other fieldsHigh correlation
_embedded.show.type is highly correlated with _embedded.show.id and 11 other fieldsHigh correlation
df_index is highly correlated with _embedded.show.language and 6 other fieldsHigh correlation
_embedded.show.rating.average is highly correlated with df_index and 10 other fieldsHigh correlation
_embedded.show.weight is highly correlated with _embedded.show.id and 10 other fieldsHigh correlation
_embedded.show.externals.tvrage is highly correlated with df_index and 13 other fieldsHigh correlation
_embedded.show.updated is highly correlated with _embedded.show.id and 10 other fieldsHigh correlation
_embedded.show.webChannel.id is highly correlated with _embedded.show.language and 8 other fieldsHigh correlation
_embedded.show.network.id is highly correlated with _embedded.show.id and 15 other fieldsHigh correlation
image.medium has 2065 (67.0%) missing values Missing
_embedded.show.webChannel.id has 85 (2.8%) missing values Missing
_embedded.show.summary has 313 (10.2%) missing values Missing
_embedded.show.runtime has 1017 (33.0%) missing values Missing
_embedded.show.rating.average has 2661 (86.3%) missing values Missing
_embedded.show.officialSite has 408 (13.2%) missing values Missing
_embedded.show.network.id has 2865 (93.0%) missing values Missing
_embedded.show.language has 34 (1.1%) missing values Missing
_embedded.show.image.original has 155 (5.0%) missing values Missing
_embedded.show.image has 3082 (100.0%) missing values Missing
_embedded.show.externals.tvrage has 3025 (98.2%) missing values Missing
_embedded.show.externals.thetvdb has 916 (29.7%) missing values Missing
_embedded.show.externals.imdb has 1471 (47.7%) missing values Missing
_embedded.show.ended has 1699 (55.1%) missing values Missing
_embedded.show.dvdCountry.timezone has 3055 (99.1%) missing values Missing
_embedded.show.dvdCountry.name has 3055 (99.1%) missing values Missing
_embedded.show.dvdCountry.code has 3055 (99.1%) missing values Missing
_embedded.show.dvdCountry has 3082 (100.0%) missing values Missing
_embedded.show.averageRuntime has 169 (5.5%) missing values Missing
image.medium is uniformly distributed Uniform
id has unique values Unique
_embedded.show.schedule.days is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.image is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.genres is an unsupported type, check if it needs cleaning or further analysis Unsupported
_embedded.show.dvdCountry is an unsupported type, check if it needs cleaning or further analysis Unsupported
df_index has 31 (1.0%) zeros Zeros

Reproduction

Analysis started2022-09-17 18:28:19.024362
Analysis finished2022-09-17 18:29:00.369122
Duration41.34 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

image.medium
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct1017
Distinct (%)100.0%
Missing2065
Missing (%)67.0%
Memory size24.2 KiB
https://static.tvmaze.com/uploads/images/medium_landscape/378/946852.jpg
 
1
https://static.tvmaze.com/uploads/images/medium_landscape/289/723722.jpg
 
1
https://static.tvmaze.com/uploads/images/medium_landscape/289/723558.jpg
 
1
https://static.tvmaze.com/uploads/images/medium_landscape/289/723573.jpg
 
1
https://static.tvmaze.com/uploads/images/medium_landscape/289/724598.jpg
 
1
Other values (1012)
1012 

Length

Max length73
Median length72
Mean length72.04523107
Min length72

Characters and Unicode

Total characters73270
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1017 ?
Unique (%)100.0%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/medium_landscape/294/737206.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/medium_landscape/286/715105.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/medium_landscape/284/710997.jpg
4th rowhttps://static.tvmaze.com/uploads/images/medium_landscape/284/710998.jpg
5th rowhttps://static.tvmaze.com/uploads/images/medium_landscape/284/710999.jpg

Common Values

ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/medium_landscape/378/946852.jpg1
 
< 0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/289/723722.jpg1
 
< 0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/289/723558.jpg1
 
< 0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/289/723573.jpg1
 
< 0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/289/724598.jpg1
 
< 0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/289/723807.jpg1
 
< 0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/289/723200.jpg1
 
< 0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/291/727595.jpg1
 
< 0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/301/752602.jpg1
 
< 0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/364/911524.jpg1
 
< 0.1%
Other values (1007)1007
32.7%
(Missing)2065
67.0%

Length

2022-09-17T13:29:00.528543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/medium_landscape/378/946852.jpg1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/358/896916.jpg1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/317/794446.jpg1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/286/715105.jpg1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/284/710997.jpg1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/284/710998.jpg1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/284/710999.jpg1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/286/715032.jpg1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/285/714313.jpg1
 
0.1%
https://static.tvmaze.com/uploads/images/medium_landscape/286/715068.jpg1
 
0.1%
Other values (1007)1007
99.0%

Most occurring characters

ValueCountFrequency (%)
/7119
 
9.7%
a6102
 
8.3%
s5085
 
6.9%
m5085
 
6.9%
t5085
 
6.9%
p4068
 
5.6%
e4068
 
5.6%
i3051
 
4.2%
c3051
 
4.2%
.3051
 
4.2%
Other values (22)27505
37.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter51867
70.8%
Other Punctuation11187
 
15.3%
Decimal Number9199
 
12.6%
Connector Punctuation1017
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a6102
11.8%
s5085
9.8%
m5085
9.8%
t5085
9.8%
p4068
 
7.8%
e4068
 
7.8%
i3051
 
5.9%
c3051
 
5.9%
d3051
 
5.9%
l2034
 
3.9%
Other values (8)11187
21.6%
Decimal Number
ValueCountFrequency (%)
21927
20.9%
71439
15.6%
81113
12.1%
9907
9.9%
1803
8.7%
3755
 
8.2%
0712
 
7.7%
6661
 
7.2%
5447
 
4.9%
4435
 
4.7%
Other Punctuation
ValueCountFrequency (%)
/7119
63.6%
.3051
27.3%
:1017
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_1017
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin51867
70.8%
Common21403
29.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a6102
11.8%
s5085
9.8%
m5085
9.8%
t5085
9.8%
p4068
 
7.8%
e4068
 
7.8%
i3051
 
5.9%
c3051
 
5.9%
d3051
 
5.9%
l2034
 
3.9%
Other values (8)11187
21.6%
Common
ValueCountFrequency (%)
/7119
33.3%
.3051
14.3%
21927
 
9.0%
71439
 
6.7%
81113
 
5.2%
_1017
 
4.8%
:1017
 
4.8%
9907
 
4.2%
1803
 
3.8%
3755
 
3.5%
Other values (4)2255
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII73270
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/7119
 
9.7%
a6102
 
8.3%
s5085
 
6.9%
m5085
 
6.9%
t5085
 
6.9%
p4068
 
5.6%
e4068
 
5.6%
i3051
 
4.2%
c3051
 
4.2%
.3051
 
4.2%
Other values (22)27505
37.5%

id
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct3082
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2035933.641
Minimum1732625
Maximum2394632
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2022-09-17T13:29:00.773129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1732625
5-th percentile1962056.05
Q11978424.25
median1990281
Q32041999
95-th percentile2300442.95
Maximum2394632
Range662007
Interquartile range (IQR)63574.75

Descriptive statistics

Standard deviation101736.187
Coefficient of variation (CV)0.04997028633
Kurtosis2.658955326
Mean2035933.641
Median Absolute Deviation (MAD)14254.5
Skewness1.866356624
Sum6274747482
Variance1.035025175 × 1010
MonotonicityNot monotonic
2022-09-17T13:29:01.024833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19798241
 
< 0.1%
20683491
 
< 0.1%
20061621
 
< 0.1%
19803981
 
< 0.1%
19803991
 
< 0.1%
21761381
 
< 0.1%
19914491
 
< 0.1%
19927381
 
< 0.1%
19859781
 
< 0.1%
23745031
 
< 0.1%
Other values (3072)3072
99.7%
ValueCountFrequency (%)
17326251
< 0.1%
18065901
< 0.1%
18645781
< 0.1%
19104461
< 0.1%
19104471
< 0.1%
19104481
< 0.1%
19104491
< 0.1%
19200591
< 0.1%
19200601
< 0.1%
19200611
< 0.1%
ValueCountFrequency (%)
23946321
< 0.1%
23938091
< 0.1%
23938081
< 0.1%
23938071
< 0.1%
23938061
< 0.1%
23900911
< 0.1%
23900901
< 0.1%
23900891
< 0.1%
23900881
< 0.1%
23900871
< 0.1%

df_index
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct176
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.79591175
Minimum0
Maximum175
Zeros31
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2022-09-17T13:29:01.293647image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.05
Q124
median49
Q381
95-th percentile130.95
Maximum175
Range175
Interquartile range (IQR)57

Descriptive statistics

Standard deviation38.86423338
Coefficient of variation (CV)0.6965426707
Kurtosis-0.1584733197
Mean55.79591175
Median Absolute Deviation (MAD)28
Skewness0.6842089099
Sum171963
Variance1510.428636
MonotonicityNot monotonic
2022-09-17T13:29:01.551456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
031
 
1.0%
2531
 
1.0%
2731
 
1.0%
2831
 
1.0%
2931
 
1.0%
3031
 
1.0%
3131
 
1.0%
3231
 
1.0%
3331
 
1.0%
3431
 
1.0%
Other values (166)2772
89.9%
ValueCountFrequency (%)
031
1.0%
131
1.0%
231
1.0%
331
1.0%
431
1.0%
531
1.0%
631
1.0%
731
1.0%
831
1.0%
931
1.0%
ValueCountFrequency (%)
1751
 
< 0.1%
1741
 
< 0.1%
1731
 
< 0.1%
1721
 
< 0.1%
1711
 
< 0.1%
1701
 
< 0.1%
1692
0.1%
1682
0.1%
1672
0.1%
1663
0.1%

_embedded.show.weight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct98
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.11291369
Minimum0
Maximum100
Zeros15
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2022-09-17T13:29:01.819285image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q117
median29
Q348
95-th percentile91
Maximum100
Range100
Interquartile range (IQR)31

Descriptive statistics

Standard deviation26.19745452
Coefficient of variation (CV)0.7254317594
Kurtosis-0.2812810834
Mean36.11291369
Median Absolute Deviation (MAD)14
Skewness0.8438391206
Sum111300
Variance686.3066232
MonotonicityNot monotonic
2022-09-17T13:29:02.066497image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19113
 
3.7%
18112
 
3.6%
29111
 
3.6%
16100
 
3.2%
791
 
3.0%
1585
 
2.8%
479
 
2.6%
2376
 
2.5%
2471
 
2.3%
3964
 
2.1%
Other values (88)2180
70.7%
ValueCountFrequency (%)
015
 
0.5%
146
1.5%
238
1.2%
345
1.5%
479
2.6%
53
 
0.1%
611
 
0.4%
791
3.0%
854
1.8%
946
1.5%
ValueCountFrequency (%)
1004
 
0.1%
9916
 
0.5%
988
 
0.3%
9725
0.8%
9630
1.0%
959
 
0.3%
942
 
0.1%
9346
1.5%
9214
 
0.5%
9121
0.7%

_embedded.show.webChannel.id
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct140
Distinct (%)4.7%
Missing85
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean148.5395395
Minimum1
Maximum538
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2022-09-17T13:29:02.320895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q121
median104
Q3238
95-th percentile420
Maximum538
Range537
Interquartile range (IQR)217

Descriptive statistics

Standard deviation142.4876691
Coefficient of variation (CV)0.9592575116
Kurtosis-0.4610331784
Mean148.5395395
Median Absolute Deviation (MAD)83
Skewness0.8386405992
Sum445173
Variance20302.73584
MonotonicityNot monotonic
2022-09-17T13:29:02.586772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21536
17.4%
104254
 
8.2%
1235
 
7.6%
67169
 
5.5%
118139
 
4.5%
238118
 
3.8%
17385
 
2.8%
367
 
2.2%
32960
 
1.9%
37959
 
1.9%
Other values (130)1275
41.4%
(Missing)85
 
2.8%
ValueCountFrequency (%)
1235
7.6%
224
 
0.8%
367
 
2.2%
1217
 
0.6%
1538
 
1.2%
2018
 
0.6%
21536
17.4%
224
 
0.1%
268
 
0.3%
3053
 
1.7%
ValueCountFrequency (%)
53810
0.3%
5334
 
0.1%
5291
 
< 0.1%
5189
0.3%
51617
0.6%
5109
0.3%
5075
 
0.2%
5061
 
< 0.1%
4985
 
0.2%
4933
 
0.1%

_embedded.show.url
Categorical

HIGH CARDINALITY

Distinct633
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Memory size24.2 KiB
https://www.tvmaze.com/shows/15250/the-young-turks
 
38
https://www.tvmaze.com/shows/55355/90-day-fiance-extras
 
32
https://www.tvmaze.com/shows/30606/scishow
 
31
https://www.tvmaze.com/shows/52743/the-penalty-zone
 
30
https://www.tvmaze.com/shows/52104/twisted-fate-of-love
 
29
Other values (628)
2922 

Length

Max length85
Median length70
Mean length50.89649578
Min length38

Characters and Unicode

Total characters156863
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122 ?
Unique (%)4.0%

Sample

1st rowhttps://www.tvmaze.com/shows/41648/sim-for-you
2nd rowhttps://www.tvmaze.com/shows/52198/kotiki
3rd rowhttps://www.tvmaze.com/shows/52933/lab-s-antonom-belaevym
4th rowhttps://www.tvmaze.com/shows/51336/core-sense
5th rowhttps://www.tvmaze.com/shows/54033/wu-shen-zhu-zai

Common Values

ValueCountFrequency (%)
https://www.tvmaze.com/shows/15250/the-young-turks38
 
1.2%
https://www.tvmaze.com/shows/55355/90-day-fiance-extras32
 
1.0%
https://www.tvmaze.com/shows/30606/scishow31
 
1.0%
https://www.tvmaze.com/shows/52743/the-penalty-zone30
 
1.0%
https://www.tvmaze.com/shows/52104/twisted-fate-of-love29
 
0.9%
https://www.tvmaze.com/shows/52421/you-complete-me28
 
0.9%
https://www.tvmaze.com/shows/47912/the-wolf26
 
0.8%
https://www.tvmaze.com/shows/52806/ultimate-note26
 
0.8%
https://www.tvmaze.com/shows/52524/forever-love24
 
0.8%
https://www.tvmaze.com/shows/52655/the-case-solver24
 
0.8%
Other values (623)2794
90.7%

Length

2022-09-17T13:29:02.881741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tvmaze.com/shows/15250/the-young-turks38
 
1.2%
https://www.tvmaze.com/shows/55355/90-day-fiance-extras32
 
1.0%
https://www.tvmaze.com/shows/30606/scishow31
 
1.0%
https://www.tvmaze.com/shows/52743/the-penalty-zone30
 
1.0%
https://www.tvmaze.com/shows/52104/twisted-fate-of-love29
 
0.9%
https://www.tvmaze.com/shows/52421/you-complete-me28
 
0.9%
https://www.tvmaze.com/shows/47912/the-wolf26
 
0.8%
https://www.tvmaze.com/shows/52806/ultimate-note26
 
0.8%
https://www.tvmaze.com/shows/52524/forever-love24
 
0.8%
https://www.tvmaze.com/shows/52655/the-case-solver24
 
0.8%
Other values (623)2794
90.7%

Most occurring characters

ValueCountFrequency (%)
/15410
 
9.8%
w13063
 
8.3%
t12514
 
8.0%
s12112
 
7.7%
o9324
 
5.9%
e8458
 
5.4%
h7759
 
4.9%
m7438
 
4.7%
a6555
 
4.2%
.6164
 
3.9%
Other values (30)58066
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter110878
70.7%
Other Punctuation24656
 
15.7%
Decimal Number15699
 
10.0%
Dash Punctuation5630
 
3.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w13063
11.8%
t12514
11.3%
s12112
10.9%
o9324
 
8.4%
e8458
 
7.6%
h7759
 
7.0%
m7438
 
6.7%
a6555
 
5.9%
c4213
 
3.8%
p3819
 
3.4%
Other values (16)25623
23.1%
Decimal Number
ValueCountFrequency (%)
52967
18.9%
21949
12.4%
41868
11.9%
11576
10.0%
01371
8.7%
61336
8.5%
31320
8.4%
91170
 
7.5%
81075
 
6.8%
71067
 
6.8%
Other Punctuation
ValueCountFrequency (%)
/15410
62.5%
.6164
 
25.0%
:3082
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
-5630
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin110878
70.7%
Common45985
29.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
w13063
11.8%
t12514
11.3%
s12112
10.9%
o9324
 
8.4%
e8458
 
7.6%
h7759
 
7.0%
m7438
 
6.7%
a6555
 
5.9%
c4213
 
3.8%
p3819
 
3.4%
Other values (16)25623
23.1%
Common
ValueCountFrequency (%)
/15410
33.5%
.6164
 
13.4%
-5630
 
12.2%
:3082
 
6.7%
52967
 
6.5%
21949
 
4.2%
41868
 
4.1%
11576
 
3.4%
01371
 
3.0%
61336
 
2.9%
Other values (4)4632
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII156863
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/15410
 
9.8%
w13063
 
8.3%
t12514
 
8.0%
s12112
 
7.7%
o9324
 
5.9%
e8458
 
5.4%
h7759
 
4.9%
m7438
 
4.7%
a6555
 
4.2%
.6164
 
3.9%
Other values (30)58066
37.0%

_embedded.show.updated
Real number (ℝ≥0)

HIGH CORRELATION

Distinct633
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1640180054
Minimum1602172227
Maximum1663430644
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2022-09-17T13:29:03.153242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1602172227
5-th percentile1608999738
Q11619633499
median1646682390
Q31655968985
95-th percentile1663236310
Maximum1663430644
Range61258417
Interquartile range (IQR)36335486

Descriptive statistics

Standard deviation19218294.68
Coefficient of variation (CV)0.01171718595
Kurtosis-1.255122597
Mean1640180054
Median Absolute Deviation (MAD)13820886
Skewness-0.4792030928
Sum5.055034926 × 1012
Variance3.693428504 × 1014
MonotonicityNot monotonic
2022-09-17T13:29:03.439202image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
164819005838
 
1.2%
164011241532
 
1.0%
164476200531
 
1.0%
165497641130
 
1.0%
160953514129
 
0.9%
161963349928
 
0.9%
164821702926
 
0.8%
164917808426
 
0.8%
161247814524
 
0.8%
165497608624
 
0.8%
Other values (623)2794
90.7%
ValueCountFrequency (%)
16021722272
 
0.1%
16034670374
0.1%
16045871195
0.2%
16045871454
0.1%
16071040924
0.1%
16071675852
 
0.1%
16072788878
0.3%
16073820732
 
0.1%
16074646181
 
< 0.1%
16075487681
 
< 0.1%
ValueCountFrequency (%)
16634306442
 
0.1%
166342849719
0.6%
16634261815
 
0.2%
166342558919
0.6%
16634254525
 
0.2%
16634236175
 
0.2%
16634035001
 
< 0.1%
16633991151
 
< 0.1%
16633665298
0.3%
16633592701
 
< 0.1%

_embedded.show.type
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size24.2 KiB
Scripted
1593 
Animation
385 
Documentary
328 
Reality
264 
Talk Show
242 
Other values (6)
270 

Length

Max length11
Median length8
Mean length8.308241402
Min length4

Characters and Unicode

Total characters25606
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReality
2nd rowScripted
3rd rowDocumentary
4th rowAnimation
5th rowAnimation

Common Values

ValueCountFrequency (%)
Scripted1593
51.7%
Animation385
 
12.5%
Documentary328
 
10.6%
Reality264
 
8.6%
Talk Show242
 
7.9%
Variety79
 
2.6%
Sports79
 
2.6%
News56
 
1.8%
Game Show48
 
1.6%
Award Show6
 
0.2%

Length

2022-09-17T13:29:03.699555image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
scripted1593
47.1%
animation385
 
11.4%
documentary328
 
9.7%
show298
 
8.8%
reality264
 
7.8%
talk242
 
7.2%
variety79
 
2.3%
sports79
 
2.3%
news56
 
1.7%
game48
 
1.4%
Other values (2)8
 
0.2%

Most occurring characters

ValueCountFrequency (%)
t2728
10.7%
i2706
10.6%
e2370
 
9.3%
r2085
 
8.1%
S1970
 
7.7%
c1921
 
7.5%
p1672
 
6.5%
d1599
 
6.2%
a1354
 
5.3%
n1100
 
4.3%
Other values (18)6101
23.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter21928
85.6%
Uppercase Letter3380
 
13.2%
Space Separator298
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t2728
12.4%
i2706
12.3%
e2370
10.8%
r2085
9.5%
c1921
8.8%
p1672
7.6%
d1599
7.3%
a1354
6.2%
n1100
 
5.0%
o1090
 
5.0%
Other values (8)3303
15.1%
Uppercase Letter
ValueCountFrequency (%)
S1970
58.3%
A391
 
11.6%
D328
 
9.7%
R264
 
7.8%
T242
 
7.2%
V79
 
2.3%
N56
 
1.7%
G48
 
1.4%
P2
 
0.1%
Space Separator
ValueCountFrequency (%)
298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin25308
98.8%
Common298
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t2728
10.8%
i2706
10.7%
e2370
9.4%
r2085
 
8.2%
S1970
 
7.8%
c1921
 
7.6%
p1672
 
6.6%
d1599
 
6.3%
a1354
 
5.4%
n1100
 
4.3%
Other values (17)5803
22.9%
Common
ValueCountFrequency (%)
298
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII25606
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t2728
10.7%
i2706
10.6%
e2370
 
9.3%
r2085
 
8.1%
S1970
 
7.7%
c1921
 
7.5%
p1672
 
6.5%
d1599
 
6.2%
a1354
 
5.3%
n1100
 
4.3%
Other values (18)6101
23.8%

_embedded.show.summary
Categorical

HIGH CARDINALITY
MISSING

Distinct556
Distinct (%)20.1%
Missing313
Missing (%)10.2%
Memory size24.2 KiB
<p>A daring, funny, and brutally honest show that covers politics, entertainment, movies, sports, and pop culture.</p>
 
38
<p>Relive some of the memorable moments our couples have faced on their journeys so far. From whopping age gaps to complex language barriers, explore their ups and downs as they prepared to embark on their new lives together.</p>
 
32
<p><b>SciShow</b> explores the unexpected. Seven days a week, Hank Green, Michael Aranda, and Olivia Gordon delve into the scientific subjects that defy our expectations and make us even more curious!</p><p>Schedule:</p><p>Sundays — Learn about the amazing topics we can't quite make a stand-alone show about in SciShow List Show!</p><p>Mondays — Tune in for a short Dose about our weird world.</p><p>Tuesdays — Find answers to our most asked Quick Questions.</p><p>Wednesdays — Hank or Michael dives deep into a long-form Infusion episode, or an unscripted talk show or quiz show with a guest!</p><p>Thursday — Another new dose about the wonders of the world.</p><p>Fridays — Learn the latest in science News.</p><p>Saturdays — Get your quick questions answered!</p>
 
31
<p>A story that follows undercover cop Gan Tian Lei who spent 10 years of his life walking a gray area. After waking up from a serious injury, he restarts his life and tries to solve a case by relying on his lost memories.</p>
 
30
<p>During the Yin Dynasty, Dong Yue, a brave general in the Dingyuan Rebellion, was sent back in time to stop a war that would claim the lives of countless innocents. She sets out to murder corrupted officer Lu Yuantong in an attempt to prevent war, and during her journey she met Feng Xi and Pang Yu. Pang Yu and Feng Xi were old friends who cared deeply for each other, but fell out and turn into enemies. While trying to reconcile the two brothers, Dong Yue also tries to stop Lu Yuantang's evil schemes which are poised to tear the nation apart with their help.</p>
 
29
Other values (551)
2609 

Length

Max length1620
Median length586
Mean length365.9180209
Min length36

Characters and Unicode

Total characters1013227
Distinct characters177
Distinct categories15 ?
Distinct scripts5 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique101 ?
Unique (%)3.6%

Sample

1st row<p><b>Sim for You</b> is a reality series that chronicles each EXO member's life and reveal stories from their everyday life as a series.</p>
2nd row<p>Russian music artists reveal themselves from unexpected sides in the Anton Belyaev's show.</p>
3rd row<p>The power of beginnings, the energy of the core stone; one may find it good, one may find it evil. During a normal investigation, Yue Juntian finds himself drawn into the battle between the 'beginnings' of Yun City; Jiang Xin arrives in Yun City to stop Li Zunyuan's plan to take over. The two influence each other - one solves the mystery of their birth, the other redeems themselves. Together, they oppose Li Zunyuan.<br /> </p>
4th row<p>The protagonist Qin Chen, who was originally the top genius in the military domain, was conspired by the people to fall into the death canyon in the forbidden land of the mainland. Qin Chen, who was inevitably dead, unexpectedly triggered the power of the mysterious ancient sword.<br /><br />Three hundred years later, in a remote part of the Tianwu mainland, a boy of the same name accidentally inherited Qin Chen's will. As the beloved grandson of King Dingwu of the Daqi National Army, due to the birth father's birth, the mother and son were treated coldly in Dingwu's palace and lived together. In order to rewrite the myth of the strong man in hope of the sun, and to protect everything he loves, Qin Chen resolutely took up the responsibility of maintaining the five kingdoms of the world and set foot on the road of martial arts again.</p>
5th row<p>It's spin-off drama of <b>"Kono Koi Atatamemasu ka"</b></p>

Common Values

ValueCountFrequency (%)
<p>A daring, funny, and brutally honest show that covers politics, entertainment, movies, sports, and pop culture.</p>38
 
1.2%
<p>Relive some of the memorable moments our couples have faced on their journeys so far. From whopping age gaps to complex language barriers, explore their ups and downs as they prepared to embark on their new lives together.</p>32
 
1.0%
<p><b>SciShow</b> explores the unexpected. Seven days a week, Hank Green, Michael Aranda, and Olivia Gordon delve into the scientific subjects that defy our expectations and make us even more curious!</p><p>Schedule:</p><p>Sundays — Learn about the amazing topics we can't quite make a stand-alone show about in SciShow List Show!</p><p>Mondays — Tune in for a short Dose about our weird world.</p><p>Tuesdays — Find answers to our most asked Quick Questions.</p><p>Wednesdays — Hank or Michael dives deep into a long-form Infusion episode, or an unscripted talk show or quiz show with a guest!</p><p>Thursday — Another new dose about the wonders of the world.</p><p>Fridays — Learn the latest in science News.</p><p>Saturdays — Get your quick questions answered!</p>31
 
1.0%
<p>A story that follows undercover cop Gan Tian Lei who spent 10 years of his life walking a gray area. After waking up from a serious injury, he restarts his life and tries to solve a case by relying on his lost memories.</p>30
 
1.0%
<p>During the Yin Dynasty, Dong Yue, a brave general in the Dingyuan Rebellion, was sent back in time to stop a war that would claim the lives of countless innocents. She sets out to murder corrupted officer Lu Yuantong in an attempt to prevent war, and during her journey she met Feng Xi and Pang Yu. Pang Yu and Feng Xi were old friends who cared deeply for each other, but fell out and turn into enemies. While trying to reconcile the two brothers, Dong Yue also tries to stop Lu Yuantang's evil schemes which are poised to tear the nation apart with their help.</p>29
 
0.9%
<p>‎At the end of the 20th century, due to the sudden decision of Xin Shensheng, gao Shan's business went bankrupt. Gao Shan wants to prove his father's innocence, but on his way suddenly falls in love with the daughter of Xin Shensheng, Tsin Waugh. Learning about the intentions of Gao Shan, Xin Shansheng makes him quit his job. ‎<br /><br />‎Gao Shan decides to go to Hong Kong to start from scratch, where he meets a benefactor and earns his first million in his life. Under the guidance of a mentor, he goes to Beijing and becomes a well-known investor. Soon Gao Shan meets Tsin Vo, who became a financial headhunter. Can love help them find their way to each other again? ‎<br /><br />‎Based on the novel by Xiao Moli "Little Storm 1.0"‎</p>28
 
0.9%
<p>The story happens at the end of the Tang Dynasty, when Zhu Wen usurps the throne and establishes the Later Liang Dynasty, and he's known as Emperor Taizu. Ma Zhai Xing (Li Qin) is the daughter of an official and as a child, she befriends a young boy (Darren Wang) who lives in the mountain. One day when he saves a wolf, he accidentally falls over the cliff and is rescued by Zhu Wen. The authoritative figure adopts him as a godson and gives him the title Bo Wang. Ten years later, he saves the female lead per chance and finds her courage and intelligence resonant, and she likes that while he's in a position of power, he still has humility and kindness. She encourages him to fight for justice and he begins that journey by helping the people, stopping throne fights, etc. Even when they have conflicts, they will face those frankly. As they overcome obstacles and fight for justice, feelings deepen and they are able to reap their own happiness by each other's side.</p>26
 
0.8%
<p>Curious about his uncle's past, Wu Xie watched a mysterious videotape, only to find himself mixed up in an elaborate conspiracy. In his adventures, he encountered Zhang Qi Ling, Xie Yu Chen, and others. </p>26
 
0.8%
<p>The parents get divorced and Jo has to move to a new place. One day, Nordstjerna goes out, and Jo discovers that a girl with magical powers lives in the attic.</p>24
 
0.8%
<p>The play is set in the turbulent period of the Republic of China in Shanghai. In a turbulent era, the forensic doctor Che Suwei and the gentleman detective Gu Yuan are intertwined with various forces. "Deputy Inspector Kang Yichen, and the innocent and lively reporter Cao Qingluo worked together to crack out a number of weird and curious cases, and restore the truth.</p>24
 
0.8%
Other values (546)2481
80.5%
(Missing)313
 
10.2%

Length

2022-09-17T13:29:03.997791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the9624
 
5.7%
and6052
 
3.6%
a5087
 
3.0%
of4880
 
2.9%
to4460
 
2.6%
in3218
 
1.9%
is1944
 
1.1%
with1688
 
1.0%
his1380
 
0.8%
that1226
 
0.7%
Other values (7703)129555
76.6%

Most occurring characters

ValueCountFrequency (%)
166005
16.4%
e95219
 
9.4%
t64081
 
6.3%
a62774
 
6.2%
o58346
 
5.8%
n58220
 
5.7%
i55755
 
5.5%
s50661
 
5.0%
r48589
 
4.8%
h40436
 
4.0%
Other values (167)313141
30.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter767159
75.7%
Space Separator166404
 
16.4%
Uppercase Letter30000
 
3.0%
Other Punctuation27063
 
2.7%
Math Symbol17202
 
1.7%
Dash Punctuation2238
 
0.2%
Decimal Number2219
 
0.2%
Format268
 
< 0.1%
Open Punctuation250
 
< 0.1%
Close Punctuation250
 
< 0.1%
Other values (5)174
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e95219
12.4%
t64081
 
8.4%
a62774
 
8.2%
o58346
 
7.6%
n58220
 
7.6%
i55755
 
7.3%
s50661
 
6.6%
r48589
 
6.3%
h40436
 
5.3%
l30922
 
4.0%
Other values (67)202156
26.4%
Uppercase Letter
ValueCountFrequency (%)
T3143
 
10.5%
S2850
 
9.5%
A2190
 
7.3%
W1603
 
5.3%
C1462
 
4.9%
M1460
 
4.9%
L1417
 
4.7%
H1407
 
4.7%
Y1264
 
4.2%
I1153
 
3.8%
Other values (27)12051
40.2%
Other Letter
ValueCountFrequency (%)
株6
 
6.0%
式6
 
6.0%
会6
 
6.0%
社6
 
6.0%
ス6
 
6.0%
タ6
 
6.0%
ム6
 
6.0%
ダ6
 
6.0%
往4
 
4.0%
注4
 
4.0%
Other values (11)44
44.0%
Other Punctuation
ValueCountFrequency (%)
,9849
36.4%
.8226
30.4%
/4493
16.6%
'2047
 
7.6%
"945
 
3.5%
!507
 
1.9%
:392
 
1.4%
?319
 
1.2%
;138
 
0.5%
&58
 
0.2%
Other values (4)89
 
0.3%
Decimal Number
ValueCountFrequency (%)
0619
27.9%
1445
20.1%
2417
18.8%
9212
 
9.6%
5119
 
5.4%
3107
 
4.8%
897
 
4.4%
479
 
3.6%
775
 
3.4%
649
 
2.2%
Math Symbol
ValueCountFrequency (%)
>8599
50.0%
<8599
50.0%
+4
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
-1895
84.7%
—274
 
12.2%
–69
 
3.1%
Space Separator
ValueCountFrequency (%)
166005
99.8%
 399
 
0.2%
Open Punctuation
ValueCountFrequency (%)
(244
97.6%
[6
 
2.4%
Close Punctuation
ValueCountFrequency (%)
)244
97.6%
]6
 
2.4%
Currency Symbol
ValueCountFrequency (%)
$30
88.2%
₴4
 
11.8%
Format
ValueCountFrequency (%)
‎268
100.0%
Initial Punctuation
ValueCountFrequency (%)
‘28
100.0%
Modifier Letter
ValueCountFrequency (%)
ー6
100.0%
Other Symbol
ValueCountFrequency (%)
✪6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin796101
78.6%
Common215968
 
21.3%
Cyrillic1058
 
0.1%
Han76
 
< 0.1%
Katakana24
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e95219
12.0%
t64081
 
8.0%
a62774
 
7.9%
o58346
 
7.3%
n58220
 
7.3%
i55755
 
7.0%
s50661
 
6.4%
r48589
 
6.1%
h40436
 
5.1%
l30922
 
3.9%
Other values (65)231098
29.0%
Common
ValueCountFrequency (%)
166005
76.9%
,9849
 
4.6%
>8599
 
4.0%
<8599
 
4.0%
.8226
 
3.8%
/4493
 
2.1%
'2047
 
0.9%
-1895
 
0.9%
"945
 
0.4%
0619
 
0.3%
Other values (32)4691
 
2.2%
Cyrillic
ValueCountFrequency (%)
т99
 
9.4%
и97
 
9.2%
е95
 
9.0%
о95
 
9.0%
а78
 
7.4%
с66
 
6.2%
н62
 
5.9%
м56
 
5.3%
к44
 
4.2%
в39
 
3.7%
Other values (29)327
30.9%
Han
ValueCountFrequency (%)
株6
 
7.9%
式6
 
7.9%
会6
 
7.9%
社6
 
7.9%
往4
 
5.3%
注4
 
5.3%
澳4
 
5.3%
门4
 
5.3%
事4
 
5.3%
孤4
 
5.3%
Other values (7)28
36.8%
Katakana
ValueCountFrequency (%)
ス6
25.0%
タ6
25.0%
ム6
25.0%
ダ6
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1010719
99.8%
Cyrillic1058
 
0.1%
Punctuation687
 
0.1%
None647
 
0.1%
CJK76
 
< 0.1%
Katakana30
 
< 0.1%
Dingbats6
 
< 0.1%
Currency Symbols4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
166005
16.4%
e95219
 
9.4%
t64081
 
6.3%
a62774
 
6.2%
o58346
 
5.8%
n58220
 
5.8%
i55755
 
5.5%
s50661
 
5.0%
r48589
 
4.8%
h40436
 
4.0%
Other values (75)310633
30.7%
None
ValueCountFrequency (%)
 399
61.7%
é52
 
8.0%
ä26
 
4.0%
å24
 
3.7%
ø20
 
3.1%
ö15
 
2.3%
Í12
 
1.9%
ü12
 
1.9%
ā11
 
1.7%
č10
 
1.5%
Other values (14)66
 
10.2%
Punctuation
ValueCountFrequency (%)
—274
39.9%
‎268
39.0%
–69
 
10.0%
…48
 
7.0%
‘28
 
4.1%
Cyrillic
ValueCountFrequency (%)
т99
 
9.4%
и97
 
9.2%
е95
 
9.0%
о95
 
9.0%
а78
 
7.4%
с66
 
6.2%
н62
 
5.9%
м56
 
5.3%
к44
 
4.2%
в39
 
3.7%
Other values (29)327
30.9%
Katakana
ValueCountFrequency (%)
ー6
20.0%
ス6
20.0%
タ6
20.0%
ム6
20.0%
ダ6
20.0%
CJK
ValueCountFrequency (%)
株6
 
7.9%
式6
 
7.9%
会6
 
7.9%
社6
 
7.9%
往4
 
5.3%
注4
 
5.3%
澳4
 
5.3%
门4
 
5.3%
事4
 
5.3%
孤4
 
5.3%
Other values (7)28
36.8%
Dingbats
ValueCountFrequency (%)
✪6
100.0%
Currency Symbols
ValueCountFrequency (%)
₴4
100.0%

_embedded.show.status
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.2 KiB
Ended
1383 
Running
1319 
To Be Determined
380 

Length

Max length16
Median length7
Mean length7.21219987
Min length5

Characters and Unicode

Total characters22228
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRunning
2nd rowEnded
3rd rowTo Be Determined
4th rowRunning
5th rowRunning

Common Values

ValueCountFrequency (%)
Ended1383
44.9%
Running1319
42.8%
To Be Determined380
 
12.3%

Length

2022-09-17T13:29:04.258403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-17T13:29:04.475858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
ended1383
36.0%
running1319
34.3%
to380
 
9.9%
be380
 
9.9%
determined380
 
9.9%

Most occurring characters

ValueCountFrequency (%)
n5720
25.7%
d3146
14.2%
e2903
13.1%
i1699
 
7.6%
E1383
 
6.2%
R1319
 
5.9%
u1319
 
5.9%
g1319
 
5.9%
760
 
3.4%
T380
 
1.7%
Other values (6)2280
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter17626
79.3%
Uppercase Letter3842
 
17.3%
Space Separator760
 
3.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n5720
32.5%
d3146
17.8%
e2903
16.5%
i1699
 
9.6%
u1319
 
7.5%
g1319
 
7.5%
o380
 
2.2%
t380
 
2.2%
r380
 
2.2%
m380
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
E1383
36.0%
R1319
34.3%
T380
 
9.9%
B380
 
9.9%
D380
 
9.9%
Space Separator
ValueCountFrequency (%)
760
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin21468
96.6%
Common760
 
3.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n5720
26.6%
d3146
14.7%
e2903
13.5%
i1699
 
7.9%
E1383
 
6.4%
R1319
 
6.1%
u1319
 
6.1%
g1319
 
6.1%
T380
 
1.8%
o380
 
1.8%
Other values (5)1900
 
8.9%
Common
ValueCountFrequency (%)
760
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII22228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n5720
25.7%
d3146
14.2%
e2903
13.1%
i1699
 
7.6%
E1383
 
6.2%
R1319
 
5.9%
u1319
 
5.9%
g1319
 
5.9%
760
 
3.4%
T380
 
1.7%
Other values (6)2280
 
10.3%

_embedded.show.schedule.time
Categorical

HIGH CORRELATION

Distinct45
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size24.2 KiB
2213 
20:00
284 
10:00
 
75
12:00
 
71
06:00
 
61
Other values (40)
378 

Length

Max length5
Median length0
Mean length1.409798832
Min length0

Characters and Unicode

Total characters4345
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.2%

Sample

1st row
2nd row10:00
3rd row23:45
4th row10:00
5th row10:00

Common Values

ValueCountFrequency (%)
2213
71.8%
20:00284
 
9.2%
10:0075
 
2.4%
12:0071
 
2.3%
06:0061
 
2.0%
21:0056
 
1.8%
19:0043
 
1.4%
18:0039
 
1.3%
00:0028
 
0.9%
22:0026
 
0.8%
Other values (35)186
 
6.0%

Length

2022-09-17T13:29:04.695052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20:00284
32.7%
10:0075
 
8.6%
12:0071
 
8.2%
06:0061
 
7.0%
21:0056
 
6.4%
19:0043
 
4.9%
18:0039
 
4.5%
00:0028
 
3.2%
22:0026
 
3.0%
17:0022
 
2.5%
Other values (34)164
18.9%

Most occurring characters

ValueCountFrequency (%)
02138
49.2%
:869
20.0%
2556
 
12.8%
1414
 
9.5%
671
 
1.6%
365
 
1.5%
861
 
1.4%
560
 
1.4%
954
 
1.2%
735
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3476
80.0%
Other Punctuation869
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
02138
61.5%
2556
 
16.0%
1414
 
11.9%
671
 
2.0%
365
 
1.9%
861
 
1.8%
560
 
1.7%
954
 
1.6%
735
 
1.0%
422
 
0.6%
Other Punctuation
ValueCountFrequency (%)
:869
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4345
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
02138
49.2%
:869
20.0%
2556
 
12.8%
1414
 
9.5%
671
 
1.6%
365
 
1.5%
861
 
1.4%
560
 
1.4%
954
 
1.2%
735
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII4345
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
02138
49.2%
:869
20.0%
2556
 
12.8%
1414
 
9.5%
671
 
1.6%
365
 
1.5%
861
 
1.4%
560
 
1.4%
954
 
1.2%
735
 
0.8%

_embedded.show.schedule.days
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size24.2 KiB

_embedded.show.runtime
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct56
Distinct (%)2.7%
Missing1017
Missing (%)33.0%
Infinite0
Infinite (%)0.0%
Mean38.52736077
Minimum1
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2022-09-17T13:29:04.935483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q120
median34
Q345
95-th percentile120
Maximum300
Range299
Interquartile range (IQR)25

Descriptive statistics

Standard deviation30.73545147
Coefficient of variation (CV)0.7977564735
Kurtosis15.48581094
Mean38.52736077
Median Absolute Deviation (MAD)14
Skewness2.987837392
Sum79559
Variance944.6679768
MonotonicityNot monotonic
2022-09-17T13:29:05.286264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45490
15.9%
30192
 
6.2%
20165
 
5.4%
60154
 
5.0%
1590
 
2.9%
12088
 
2.9%
2582
 
2.7%
4062
 
2.0%
5062
 
2.0%
1062
 
2.0%
Other values (46)618
20.1%
(Missing)1017
33.0%
ValueCountFrequency (%)
14
 
0.1%
210
 
0.3%
33
 
0.1%
418
 
0.6%
545
1.5%
67
 
0.2%
739
1.3%
838
1.2%
97
 
0.2%
1062
2.0%
ValueCountFrequency (%)
3004
 
0.1%
2403
 
0.1%
18014
 
0.5%
1304
 
0.1%
12088
2.9%
9023
 
0.7%
661
 
< 0.1%
625
 
0.2%
60154
5.0%
582
 
0.1%

_embedded.show.rating.average
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct33
Distinct (%)7.8%
Missing2661
Missing (%)86.3%
Infinite0
Infinite (%)0.0%
Mean6.864608076
Minimum3.6
Maximum8.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2022-09-17T13:29:05.538392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3.6
5-th percentile5
Q16.6
median7.1
Q37.5
95-th percentile8.1
Maximum8.8
Range5.2
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.93839105
Coefficient of variation (CV)0.1366998727
Kurtosis0.2982410094
Mean6.864608076
Median Absolute Deviation (MAD)0.4
Skewness-0.7695587937
Sum2890
Variance0.8805777627
MonotonicityNot monotonic
2022-09-17T13:29:05.780452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
7.244
 
1.4%
7.733
 
1.1%
7.532
 
1.0%
6.829
 
0.9%
6.728
 
0.9%
7.422
 
0.7%
721
 
0.7%
521
 
0.7%
6.620
 
0.6%
7.315
 
0.5%
Other values (23)156
 
5.1%
(Missing)2661
86.3%
ValueCountFrequency (%)
3.62
 
0.1%
41
 
< 0.1%
4.31
 
< 0.1%
4.82
 
0.1%
521
0.7%
5.28
 
0.3%
5.314
0.5%
5.412
0.4%
5.64
 
0.1%
5.88
 
0.3%
ValueCountFrequency (%)
8.85
 
0.2%
8.63
 
0.1%
8.212
 
0.4%
8.113
 
0.4%
85
 
0.2%
7.810
 
0.3%
7.733
1.1%
7.64
 
0.1%
7.532
1.0%
7.422
0.7%

_embedded.show.premiered
Categorical

HIGH CARDINALITY

Distinct407
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Memory size24.2 KiB
2020-12-04
 
101
2020-12-10
 
75
2020-12-14
 
65
2020-11-19
 
62
2020-12-16
 
60
Other values (402)
2719 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters30820
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)2.5%

Sample

1st row2019-03-25
2nd row2020-11-30
3rd row2019-12-17
4th row2020-10-13
5th row2020-03-08

Common Values

ValueCountFrequency (%)
2020-12-04101
 
3.3%
2020-12-1075
 
2.4%
2020-12-1465
 
2.1%
2020-11-1962
 
2.0%
2020-12-1660
 
1.9%
2020-12-2158
 
1.9%
2020-11-2358
 
1.9%
2020-12-0756
 
1.8%
2020-12-0154
 
1.8%
2020-12-1854
 
1.8%
Other values (397)2439
79.1%

Length

2022-09-17T13:29:05.985182image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-04101
 
3.3%
2020-12-1075
 
2.4%
2020-12-1465
 
2.1%
2020-11-1962
 
2.0%
2020-12-1660
 
1.9%
2020-12-2158
 
1.9%
2020-11-2358
 
1.9%
2020-12-0756
 
1.8%
2020-12-0154
 
1.8%
2020-12-1854
 
1.8%
Other values (397)2439
79.1%

Most occurring characters

ValueCountFrequency (%)
07653
24.8%
27488
24.3%
-6164
20.0%
15318
17.3%
9910
 
3.0%
3665
 
2.2%
8642
 
2.1%
4613
 
2.0%
7549
 
1.8%
6409
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number24656
80.0%
Dash Punctuation6164
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
07653
31.0%
27488
30.4%
15318
21.6%
9910
 
3.7%
3665
 
2.7%
8642
 
2.6%
4613
 
2.5%
7549
 
2.2%
6409
 
1.7%
5409
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
-6164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common30820
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
07653
24.8%
27488
24.3%
-6164
20.0%
15318
17.3%
9910
 
3.0%
3665
 
2.2%
8642
 
2.1%
4613
 
2.0%
7549
 
1.8%
6409
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII30820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
07653
24.8%
27488
24.3%
-6164
20.0%
15318
17.3%
9910
 
3.0%
3665
 
2.2%
8642
 
2.1%
4613
 
2.0%
7549
 
1.8%
6409
 
1.3%

_embedded.show.officialSite
Categorical

HIGH CARDINALITY
MISSING

Distinct570
Distinct (%)21.3%
Missing408
Missing (%)13.2%
Memory size24.2 KiB
https://www.tytnetwork.com
 
38
https://www.discoveryplus.co.uk/show/90-day-extras
 
32
https://www.youtube.com/user/scishow/
 
31
https://www.iqiyi.com/a_19rrhllpip.html
 
30
https://v.qq.com/x/search/?q=+%E4%BB%8A%E5%A4%95%E4%BD%95%E5%A4%95&stag=0&smartbox_ab=
 
29
Other values (565)
2514 

Length

Max length250
Median length89
Mean length51.23635004
Min length15

Characters and Unicode

Total characters137006
Distinct characters77
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique111 ?
Unique (%)4.2%

Sample

1st rowhttps://www.vlive.tv/video/121637
2nd rowhttp://epic-media.ru/project/kotiki
3rd rowhttps://premier.one/show/lab-laboratoriya-muzyki-antona-belyaeva
4th rowhttps://www.bilibili.com/bangumi/media/md28223064
5th rowhttps://v.qq.com/detail/m/7q544xyrava3vxf.html

Common Values

ValueCountFrequency (%)
https://www.tytnetwork.com38
 
1.2%
https://www.discoveryplus.co.uk/show/90-day-extras32
 
1.0%
https://www.youtube.com/user/scishow/31
 
1.0%
https://www.iqiyi.com/a_19rrhllpip.html30
 
1.0%
https://v.qq.com/x/search/?q=+%E4%BB%8A%E5%A4%95%E4%BD%95%E5%A4%95&stag=0&smartbox_ab=29
 
0.9%
https://www.iqiyi.com/a_nvzsmw0tgx.html26
 
0.8%
https://www.iqiyi.com/lib/m_213579814.html26
 
0.8%
https://tv.nrk.no/serie/stjernestoev24
 
0.8%
https://v.qq.com/detail/m/mzc00200dnvb1wh.html24
 
0.8%
https://www.iqiyi.com/a_c4m3iuc94t.html24
 
0.8%
Other values (560)2390
77.5%
(Missing)408
 
13.2%

Length

2022-09-17T13:29:06.229290image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.tytnetwork.com38
 
1.4%
https://www.discoveryplus.co.uk/show/90-day-extras32
 
1.2%
https://www.youtube.com/user/scishow31
 
1.2%
https://www.iqiyi.com/a_19rrhllpip.html30
 
1.1%
https://v.qq.com/x/search/?q=+%e4%bb%8a%e5%a4%95%e4%bd%95%e5%a4%95&stag=0&smartbox_ab29
 
1.1%
https://www.iqiyi.com/a_nvzsmw0tgx.html26
 
1.0%
https://www.iqiyi.com/lib/m_213579814.html26
 
1.0%
https://tv.nrk.no/serie/stjernestoev24
 
0.9%
https://v.qq.com/detail/m/mzc00200dnvb1wh.html24
 
0.9%
https://www.iqiyi.com/a_c4m3iuc94t.html24
 
0.9%
Other values (559)2390
89.4%

Most occurring characters

ValueCountFrequency (%)
/11240
 
8.2%
t10538
 
7.7%
s6976
 
5.1%
e6915
 
5.0%
w6070
 
4.4%
o5897
 
4.3%
.5648
 
4.1%
h5123
 
3.7%
i4793
 
3.5%
p4441
 
3.2%
Other values (67)69365
50.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter90251
65.9%
Other Punctuation21749
 
15.9%
Decimal Number13874
 
10.1%
Uppercase Letter7405
 
5.4%
Dash Punctuation2421
 
1.8%
Math Symbol735
 
0.5%
Connector Punctuation571
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t10538
 
11.7%
s6976
 
7.7%
e6915
 
7.7%
w6070
 
6.7%
o5897
 
6.5%
h5123
 
5.7%
i4793
 
5.3%
p4441
 
4.9%
a4332
 
4.8%
m4082
 
4.5%
Other values (16)31084
34.4%
Uppercase Letter
ValueCountFrequency (%)
E602
 
8.1%
A574
 
7.8%
B513
 
6.9%
P460
 
6.2%
C432
 
5.8%
D365
 
4.9%
L364
 
4.9%
N309
 
4.2%
T279
 
3.8%
M268
 
3.6%
Other values (16)3239
43.7%
Other Punctuation
ValueCountFrequency (%)
/11240
51.7%
.5648
26.0%
:2930
 
13.5%
%1288
 
5.9%
?382
 
1.8%
&199
 
0.9%
!21
 
0.1%
#21
 
0.1%
,10
 
< 0.1%
'10
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
02108
15.2%
11781
12.8%
81432
10.3%
21392
10.0%
41384
10.0%
91352
9.7%
31243
9.0%
51166
8.4%
61058
7.6%
7958
6.9%
Math Symbol
ValueCountFrequency (%)
=676
92.0%
+43
 
5.9%
~16
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
-2421
100.0%
Connector Punctuation
ValueCountFrequency (%)
_571
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin97656
71.3%
Common39350
28.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t10538
 
10.8%
s6976
 
7.1%
e6915
 
7.1%
w6070
 
6.2%
o5897
 
6.0%
h5123
 
5.2%
i4793
 
4.9%
p4441
 
4.5%
a4332
 
4.4%
m4082
 
4.2%
Other values (42)38489
39.4%
Common
ValueCountFrequency (%)
/11240
28.6%
.5648
14.4%
:2930
 
7.4%
-2421
 
6.2%
02108
 
5.4%
11781
 
4.5%
81432
 
3.6%
21392
 
3.5%
41384
 
3.5%
91352
 
3.4%
Other values (15)7662
19.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII137006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/11240
 
8.2%
t10538
 
7.7%
s6976
 
5.1%
e6915
 
5.0%
w6070
 
4.4%
o5897
 
4.3%
.5648
 
4.1%
h5123
 
3.7%
i4793
 
3.5%
p4441
 
3.2%
Other values (67)69365
50.6%

_embedded.show.network.id
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct43
Distinct (%)19.8%
Missing2865
Missing (%)93.0%
Infinite0
Infinite (%)0.0%
Mean453.6082949
Minimum8
Maximum1862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2022-09-17T13:29:06.464923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile23
Q1112
median276
Q3514
95-th percentile1808
Maximum1862
Range1854
Interquartile range (IQR)402

Descriptive statistics

Standard deviation523.412192
Coefficient of variation (CV)1.153885848
Kurtosis1.557999885
Mean453.6082949
Median Absolute Deviation (MAD)170
Skewness1.656339883
Sum98433
Variance273960.3227
MonotonicityNot monotonic
2022-09-17T13:29:06.688063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
11219
 
0.6%
37417
 
0.6%
180811
 
0.4%
51411
 
0.4%
3010
 
0.3%
55110
 
0.3%
1329
 
0.3%
238
 
0.3%
3398
 
0.3%
3088
 
0.3%
Other values (33)106
 
3.4%
(Missing)2865
93.0%
ValueCountFrequency (%)
84
 
0.1%
238
0.3%
3010
0.3%
371
 
< 0.1%
413
 
0.1%
491
 
< 0.1%
514
 
0.1%
763
 
0.1%
785
0.2%
853
 
0.1%
ValueCountFrequency (%)
18623
 
0.1%
180811
0.4%
17663
 
0.1%
16832
 
0.1%
15691
 
< 0.1%
13544
 
0.1%
13204
 
0.1%
12821
 
< 0.1%
12623
 
0.1%
10062
 
0.1%

_embedded.show.name
Categorical

HIGH CARDINALITY

Distinct631
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Memory size24.2 KiB
The Young Turks
 
38
90 Day Fiancé: Extras
 
32
SciShow
 
31
The Penalty Zone
 
30
Twisted Fate of Love
 
29
Other values (626)
2922 

Length

Max length51
Median length37
Mean length16.17780662
Min length3

Characters and Unicode

Total characters49860
Distinct characters173
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122 ?
Unique (%)4.0%

Sample

1st rowSim for You
2nd rowКотики
3rd rowLAB с Антоном Беляевым
4th rowCore Sense
5th rowWu Shen Zhu Zai

Common Values

ValueCountFrequency (%)
The Young Turks38
 
1.2%
90 Day Fiancé: Extras32
 
1.0%
SciShow31
 
1.0%
The Penalty Zone30
 
1.0%
Twisted Fate of Love29
 
0.9%
You Complete Me28
 
0.9%
The Wolf26
 
0.8%
Ultimate Note26
 
0.8%
Forever Love24
 
0.8%
The Case Solver24
 
0.8%
Other values (621)2794
90.7%

Length

2022-09-17T13:29:06.953022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the464
 
5.3%
of190
 
2.2%
love124
 
1.4%
you79
 
0.9%
in68
 
0.8%
a61
 
0.7%
to51
 
0.6%
my49
 
0.6%
with44
 
0.5%
me44
 
0.5%
Other values (1297)7524
86.5%

Most occurring characters

ValueCountFrequency (%)
5616
 
11.3%
e4797
 
9.6%
a2784
 
5.6%
o2669
 
5.4%
i2457
 
4.9%
n2433
 
4.9%
r2403
 
4.8%
t2207
 
4.4%
s1968
 
3.9%
l1528
 
3.1%
Other values (163)20998
42.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter35188
70.6%
Uppercase Letter7897
 
15.8%
Space Separator5616
 
11.3%
Other Punctuation706
 
1.4%
Decimal Number376
 
0.8%
Dash Punctuation57
 
0.1%
Close Punctuation8
 
< 0.1%
Currency Symbol8
 
< 0.1%
Open Punctuation4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e4797
13.6%
a2784
 
7.9%
o2669
 
7.6%
i2457
 
7.0%
n2433
 
6.9%
r2403
 
6.8%
t2207
 
6.3%
s1968
 
5.6%
l1528
 
4.3%
h1319
 
3.7%
Other values (75)10623
30.2%
Uppercase Letter
ValueCountFrequency (%)
T904
 
11.4%
S677
 
8.6%
B522
 
6.6%
M477
 
6.0%
L437
 
5.5%
C416
 
5.3%
W412
 
5.2%
F386
 
4.9%
D364
 
4.6%
A353
 
4.5%
Other values (49)2949
37.3%
Other Punctuation
ValueCountFrequency (%)
:257
36.4%
'150
21.2%
.102
 
14.4%
!69
 
9.8%
,50
 
7.1%
?34
 
4.8%
&22
 
3.1%
%8
 
1.1%
#6
 
0.8%
@4
 
0.6%
Other values (2)4
 
0.6%
Decimal Number
ValueCountFrequency (%)
0133
35.4%
293
24.7%
342
 
11.2%
932
 
8.5%
132
 
8.5%
715
 
4.0%
514
 
3.7%
69
 
2.4%
84
 
1.1%
42
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
-54
94.7%
–3
 
5.3%
Currency Symbol
ValueCountFrequency (%)
₴4
50.0%
$4
50.0%
Space Separator
ValueCountFrequency (%)
5616
100.0%
Close Punctuation
ValueCountFrequency (%)
)8
100.0%
Open Punctuation
ValueCountFrequency (%)
(4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin40314
80.9%
Common6775
 
13.6%
Cyrillic2603
 
5.2%
Greek168
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e4797
 
11.9%
a2784
 
6.9%
o2669
 
6.6%
i2457
 
6.1%
n2433
 
6.0%
r2403
 
6.0%
t2207
 
5.5%
s1968
 
4.9%
l1528
 
3.8%
h1319
 
3.3%
Other values (57)15749
39.1%
Cyrillic
ValueCountFrequency (%)
о237
 
9.1%
е197
 
7.6%
а190
 
7.3%
и176
 
6.8%
к148
 
5.7%
н145
 
5.6%
т138
 
5.3%
р129
 
5.0%
с108
 
4.1%
м70
 
2.7%
Other values (50)1065
40.9%
Common
ValueCountFrequency (%)
5616
82.9%
:257
 
3.8%
'150
 
2.2%
0133
 
2.0%
.102
 
1.5%
293
 
1.4%
!69
 
1.0%
-54
 
0.8%
,50
 
0.7%
342
 
0.6%
Other values (19)209
 
3.1%
Greek
ValueCountFrequency (%)
ς24
14.3%
ε16
 
9.5%
έ16
 
9.5%
Έ8
 
4.8%
Ε8
 
4.8%
Χ8
 
4.8%
γ8
 
4.8%
α8
 
4.8%
ο8
 
4.8%
ρ8
 
4.8%
Other values (7)56
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII46854
94.0%
Cyrillic2603
 
5.2%
None396
 
0.8%
Currency Symbols4
 
< 0.1%
Punctuation3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5616
 
12.0%
e4797
 
10.2%
a2784
 
5.9%
o2669
 
5.7%
i2457
 
5.2%
n2433
 
5.2%
r2403
 
5.1%
t2207
 
4.7%
s1968
 
4.2%
l1528
 
3.3%
Other values (69)17992
38.4%
Cyrillic
ValueCountFrequency (%)
о237
 
9.1%
е197
 
7.6%
а190
 
7.3%
и176
 
6.8%
к148
 
5.7%
н145
 
5.6%
т138
 
5.3%
р129
 
5.0%
с108
 
4.1%
м70
 
2.7%
Other values (50)1065
40.9%
None
ValueCountFrequency (%)
ø63
15.9%
é52
 
13.1%
ä26
 
6.6%
å25
 
6.3%
ς24
 
6.1%
ε16
 
4.0%
έ16
 
4.0%
ı11
 
2.8%
á10
 
2.5%
Ç8
 
2.0%
Other values (22)145
36.6%
Currency Symbols
ValueCountFrequency (%)
₴4
100.0%
Punctuation
ValueCountFrequency (%)
–3
100.0%

_embedded.show.language
Categorical

HIGH CORRELATION
MISSING

Distinct37
Distinct (%)1.2%
Missing34
Missing (%)1.1%
Memory size24.2 KiB
English
983 
Chinese
686 
Russian
246 
Norwegian
223 
Korean
181 
Other values (32)
729 

Length

Max length10
Median length7
Mean length6.982283465
Min length4

Characters and Unicode

Total characters21282
Distinct characters43
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKorean
2nd rowRussian
3rd rowRussian
4th rowChinese
5th rowChinese

Common Values

ValueCountFrequency (%)
English983
31.9%
Chinese686
22.3%
Russian246
 
8.0%
Norwegian223
 
7.2%
Korean181
 
5.9%
Arabic66
 
2.1%
Hindi64
 
2.1%
Japanese64
 
2.1%
Spanish60
 
1.9%
Thai51
 
1.7%
Other values (27)424
13.8%

Length

2022-09-17T13:29:07.192183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
english983
32.3%
chinese686
22.5%
russian246
 
8.1%
norwegian223
 
7.3%
korean181
 
5.9%
arabic66
 
2.2%
hindi64
 
2.1%
japanese64
 
2.1%
spanish60
 
2.0%
thai51
 
1.7%
Other values (27)424
13.9%

Most occurring characters

ValueCountFrequency (%)
n2725
12.8%
i2673
12.6%
s2446
11.5%
e2165
10.2%
h1967
9.2%
g1357
 
6.4%
a1252
 
5.9%
l1091
 
5.1%
E983
 
4.6%
C686
 
3.2%
Other values (33)3937
18.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter18234
85.7%
Uppercase Letter3048
 
14.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n2725
14.9%
i2673
14.7%
s2446
13.4%
e2165
11.9%
h1967
10.8%
g1357
7.4%
a1252
6.9%
l1091
6.0%
r648
 
3.6%
o508
 
2.8%
Other values (13)1402
7.7%
Uppercase Letter
ValueCountFrequency (%)
E983
32.3%
C686
22.5%
R250
 
8.2%
N223
 
7.3%
K185
 
6.1%
T139
 
4.6%
S91
 
3.0%
H73
 
2.4%
P69
 
2.3%
D66
 
2.2%
Other values (10)283
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Latin21282
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n2725
12.8%
i2673
12.6%
s2446
11.5%
e2165
10.2%
h1967
9.2%
g1357
 
6.4%
a1252
 
5.9%
l1091
 
5.1%
E983
 
4.6%
C686
 
3.2%
Other values (33)3937
18.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII21282
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n2725
12.8%
i2673
12.6%
s2446
11.5%
e2165
10.2%
h1967
9.2%
g1357
 
6.4%
a1252
 
5.9%
l1091
 
5.1%
E983
 
4.6%
C686
 
3.2%
Other values (33)3937
18.5%

_embedded.show.image.original
Categorical

HIGH CARDINALITY
MISSING

Distinct585
Distinct (%)20.0%
Missing155
Missing (%)5.0%
Memory size24.2 KiB
https://static.tvmaze.com/uploads/images/original_untouched/51/129595.jpg
 
38
https://static.tvmaze.com/uploads/images/original_untouched/317/794301.jpg
 
32
https://static.tvmaze.com/uploads/images/original_untouched/121/302950.jpg
 
31
https://static.tvmaze.com/uploads/images/original_untouched/291/729147.jpg
 
30
https://static.tvmaze.com/uploads/images/original_untouched/285/713040.jpg
 
29
Other values (580)
2767 

Length

Max length75
Median length74
Mean length74.02903997
Min length72

Characters and Unicode

Total characters216683
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique110 ?
Unique (%)3.8%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/original_untouched/190/476668.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/original_untouched/355/888089.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/original_untouched/379/948045.jpg
4th rowhttps://static.tvmaze.com/uploads/images/original_untouched/278/696645.jpg
5th rowhttps://static.tvmaze.com/uploads/images/original_untouched/299/748854.jpg

Common Values

ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/original_untouched/51/129595.jpg38
 
1.2%
https://static.tvmaze.com/uploads/images/original_untouched/317/794301.jpg32
 
1.0%
https://static.tvmaze.com/uploads/images/original_untouched/121/302950.jpg31
 
1.0%
https://static.tvmaze.com/uploads/images/original_untouched/291/729147.jpg30
 
1.0%
https://static.tvmaze.com/uploads/images/original_untouched/285/713040.jpg29
 
0.9%
https://static.tvmaze.com/uploads/images/original_untouched/288/721821.jpg28
 
0.9%
https://static.tvmaze.com/uploads/images/original_untouched/255/639532.jpg26
 
0.8%
https://static.tvmaze.com/uploads/images/original_untouched/291/729820.jpg26
 
0.8%
https://static.tvmaze.com/uploads/images/original_untouched/308/770106.jpg24
 
0.8%
https://static.tvmaze.com/uploads/images/original_untouched/289/723488.jpg24
 
0.8%
Other values (575)2639
85.6%
(Missing)155
 
5.0%

Length

2022-09-17T13:29:07.979425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/original_untouched/51/129595.jpg38
 
1.3%
https://static.tvmaze.com/uploads/images/original_untouched/317/794301.jpg32
 
1.1%
https://static.tvmaze.com/uploads/images/original_untouched/121/302950.jpg31
 
1.1%
https://static.tvmaze.com/uploads/images/original_untouched/291/729147.jpg30
 
1.0%
https://static.tvmaze.com/uploads/images/original_untouched/285/713040.jpg29
 
1.0%
https://static.tvmaze.com/uploads/images/original_untouched/288/721821.jpg28
 
1.0%
https://static.tvmaze.com/uploads/images/original_untouched/255/639532.jpg26
 
0.9%
https://static.tvmaze.com/uploads/images/original_untouched/291/729820.jpg26
 
0.9%
https://static.tvmaze.com/uploads/images/original_untouched/308/770106.jpg24
 
0.8%
https://static.tvmaze.com/uploads/images/original_untouched/289/723488.jpg24
 
0.8%
Other values (575)2639
90.2%

Most occurring characters

ValueCountFrequency (%)
/20489
 
9.5%
t17562
 
8.1%
a14635
 
6.8%
s11708
 
5.4%
i11708
 
5.4%
o11708
 
5.4%
p8781
 
4.1%
c8781
 
4.1%
.8781
 
4.1%
g8781
 
4.1%
Other values (23)93749
43.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter155131
71.6%
Other Punctuation32197
 
14.9%
Decimal Number26428
 
12.2%
Connector Punctuation2927
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t17562
 
11.3%
a14635
 
9.4%
s11708
 
7.5%
i11708
 
7.5%
o11708
 
7.5%
p8781
 
5.7%
c8781
 
5.7%
g8781
 
5.7%
m8781
 
5.7%
e8781
 
5.7%
Other values (9)43905
28.3%
Decimal Number
ValueCountFrequency (%)
23780
14.3%
73263
12.3%
82899
11.0%
12846
10.8%
92656
10.0%
32554
9.7%
02236
8.5%
52159
8.2%
42158
8.2%
61877
7.1%
Other Punctuation
ValueCountFrequency (%)
/20489
63.6%
.8781
27.3%
:2927
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_2927
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin155131
71.6%
Common61552
 
28.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t17562
 
11.3%
a14635
 
9.4%
s11708
 
7.5%
i11708
 
7.5%
o11708
 
7.5%
p8781
 
5.7%
c8781
 
5.7%
g8781
 
5.7%
m8781
 
5.7%
e8781
 
5.7%
Other values (9)43905
28.3%
Common
ValueCountFrequency (%)
/20489
33.3%
.8781
14.3%
23780
 
6.1%
73263
 
5.3%
:2927
 
4.8%
_2927
 
4.8%
82899
 
4.7%
12846
 
4.6%
92656
 
4.3%
32554
 
4.1%
Other values (4)8430
13.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII216683
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/20489
 
9.5%
t17562
 
8.1%
a14635
 
6.8%
s11708
 
5.4%
i11708
 
5.4%
o11708
 
5.4%
p8781
 
4.1%
c8781
 
4.1%
.8781
 
4.1%
g8781
 
4.1%
Other values (23)93749
43.3%

_embedded.show.image
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing3082
Missing (%)100.0%
Memory size24.2 KiB

_embedded.show.id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct633
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46952.47664
Minimum802
Maximum64152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2022-09-17T13:29:08.200404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum802
5-th percentile15250
Q144135
median51954
Q352806
95-th percentile60156
Maximum64152
Range63350
Interquartile range (IQR)8671

Descriptive statistics

Standard deviation12449.65797
Coefficient of variation (CV)0.2651544468
Kurtosis3.026877779
Mean46952.47664
Median Absolute Deviation (MAD)2825
Skewness-1.820832003
Sum144707533
Variance154993983.5
MonotonicityNot monotonic
2022-09-17T13:29:08.451386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1525038
 
1.2%
5535532
 
1.0%
3060631
 
1.0%
5274330
 
1.0%
5210429
 
0.9%
5242128
 
0.9%
4791226
 
0.8%
5280626
 
0.8%
5252424
 
0.8%
5265524
 
0.8%
Other values (623)2794
90.7%
ValueCountFrequency (%)
8024
 
0.1%
15962
 
0.1%
18255
 
0.2%
22666
 
0.2%
250419
0.6%
28551
 
< 0.1%
37341
 
< 0.1%
40913
 
0.1%
50581
 
< 0.1%
60904
 
0.1%
ValueCountFrequency (%)
641521
 
< 0.1%
641324
 
0.1%
640238
0.3%
639675
0.2%
637613
 
0.1%
6371910
0.3%
633104
 
0.1%
631554
 
0.1%
629012
 
0.1%
627646
0.2%

_embedded.show.genres
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size24.2 KiB

_embedded.show.externals.tvrage
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct11
Distinct (%)19.3%
Missing3025
Missing (%)98.2%
Infinite0
Infinite (%)0.0%
Mean23860.26316
Minimum5152
Maximum47170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2022-09-17T13:29:08.669617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5152
5-th percentile5152
Q119056
median19056
Q334149
95-th percentile41967
Maximum47170
Range42018
Interquartile range (IQR)15093

Descriptive statistics

Standard deviation11571.50109
Coefficient of variation (CV)0.4849695502
Kurtosis-0.6702313355
Mean23860.26316
Median Absolute Deviation (MAD)11226
Skewness0.1352026074
Sum1360035
Variance133899637.5
MonotonicityNot monotonic
2022-09-17T13:29:08.817805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1905619
 
0.6%
341498
 
0.3%
251006
 
0.2%
51525
 
0.2%
419675
 
0.2%
302824
 
0.1%
66594
 
0.1%
471702
 
0.1%
150902
 
0.1%
382921
 
< 0.1%
(Missing)3025
98.2%
ValueCountFrequency (%)
51525
 
0.2%
66594
 
0.1%
150902
 
0.1%
1905619
0.6%
251006
 
0.2%
280081
 
< 0.1%
302824
 
0.1%
341498
0.3%
382921
 
< 0.1%
419675
 
0.2%
ValueCountFrequency (%)
471702
 
0.1%
419675
 
0.2%
382921
 
< 0.1%
341498
0.3%
302824
 
0.1%
280081
 
< 0.1%
251006
 
0.2%
1905619
0.6%
150902
 
0.1%
66594
 
0.1%

_embedded.show.externals.thetvdb
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct440
Distinct (%)20.3%
Missing916
Missing (%)29.7%
Infinite0
Infinite (%)0.0%
Mean359326.4598
Minimum73246
Maximum424733
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2022-09-17T13:29:09.027512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum73246
5-th percentile265193
Q1345280
median382296
Q3392410
95-th percentile394627
Maximum424733
Range351487
Interquartile range (IQR)47130

Descriptive statistics

Standard deviation55819.43019
Coefficient of variation (CV)0.155344614
Kurtosis9.730514498
Mean359326.4598
Median Absolute Deviation (MAD)11561
Skewness-2.820957348
Sum778301112
Variance3115808786
MonotonicityNot monotonic
2022-09-17T13:29:09.265055image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27879338
 
1.2%
26519331
 
1.0%
39221429
 
0.9%
39267928
 
0.9%
39322926
 
0.8%
33109526
 
0.8%
39724724
 
0.8%
39338124
 
0.8%
39264924
 
0.8%
36979822
 
0.7%
Other values (430)1894
61.5%
(Missing)916
29.7%
ValueCountFrequency (%)
732464
 
0.1%
767794
 
0.1%
784195
 
0.2%
788964
 
0.1%
794291
 
< 0.1%
854366
 
0.2%
10427119
0.6%
1445416
 
0.2%
1449914
 
0.1%
1454311
 
< 0.1%
ValueCountFrequency (%)
4247331
 
< 0.1%
4190452
 
0.1%
4136274
0.1%
4127371
 
< 0.1%
4119233
0.1%
4101876
0.2%
4100863
0.1%
4089565
0.2%
4087602
 
0.1%
4080343
0.1%

_embedded.show.externals.imdb
Categorical

HIGH CARDINALITY
MISSING

Distinct309
Distinct (%)19.2%
Missing1471
Missing (%)47.7%
Memory size24.2 KiB
tt1714810
 
38
tt13599000
 
30
tt13568876
 
28
tt8871128
 
26
tt11492320
 
24
Other values (304)
1465 

Length

Max length10
Median length10
Mean length9.631284916
Min length9

Characters and Unicode

Total characters15516
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)3.4%

Sample

1st rowtt15127174
2nd rowtt11492320
3rd rowtt13570512
4th rowtt13570512
5th rowtt13570512

Common Values

ValueCountFrequency (%)
tt171481038
 
1.2%
tt1359900030
 
1.0%
tt1356887628
 
0.9%
tt887112826
 
0.8%
tt1149232024
 
0.8%
tt1359898824
 
0.8%
tt1245794622
 
0.7%
tt1353971020
 
0.6%
tt1193955020
 
0.6%
tt408703219
 
0.6%
Other values (299)1360
44.1%
(Missing)1471
47.7%

Length

2022-09-17T13:29:09.516972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tt171481038
 
2.4%
tt1359900030
 
1.9%
tt1356887628
 
1.7%
tt887112826
 
1.6%
tt1149232024
 
1.5%
tt1359898824
 
1.5%
tt1245794622
 
1.4%
tt1353971020
 
1.2%
tt1193955020
 
1.2%
tt408703219
 
1.2%
Other values (299)1360
84.4%

Most occurring characters

ValueCountFrequency (%)
t3222
20.8%
12065
13.3%
01422
9.2%
21313
8.5%
31250
 
8.1%
61218
 
7.8%
81206
 
7.8%
41034
 
6.7%
9959
 
6.2%
5930
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number12294
79.2%
Lowercase Letter3222
 
20.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
12065
16.8%
01422
11.6%
21313
10.7%
31250
10.2%
61218
9.9%
81206
9.8%
41034
8.4%
9959
7.8%
5930
7.6%
7897
7.3%
Lowercase Letter
ValueCountFrequency (%)
t3222
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common12294
79.2%
Latin3222
 
20.8%

Most frequent character per script

Common
ValueCountFrequency (%)
12065
16.8%
01422
11.6%
21313
10.7%
31250
10.2%
61218
9.9%
81206
9.8%
41034
8.4%
9959
7.8%
5930
7.6%
7897
7.3%
Latin
ValueCountFrequency (%)
t3222
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15516
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t3222
20.8%
12065
13.3%
01422
9.2%
21313
8.5%
31250
 
8.1%
61218
 
7.8%
81206
 
7.8%
41034
 
6.7%
9959
 
6.2%
5930
 
6.0%

_embedded.show.ended
Categorical

HIGH CARDINALITY
MISSING

Distinct112
Distinct (%)8.1%
Missing1699
Missing (%)55.1%
Memory size24.2 KiB
2020-12-18
 
61
2020-12-30
 
61
2020-12-11
 
59
2021-01-07
 
58
2020-12-16
 
57
Other values (107)
1087 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters13830
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)1.4%

Sample

1st row2020-12-11
2nd row2020-12-22
3rd row2020-12-08
4th row2020-12-08
5th row2020-12-01

Common Values

ValueCountFrequency (%)
2020-12-1861
 
2.0%
2020-12-3061
 
2.0%
2020-12-1159
 
1.9%
2021-01-0758
 
1.9%
2020-12-1657
 
1.8%
2021-01-0552
 
1.7%
2020-12-2249
 
1.6%
2020-12-2449
 
1.6%
2020-12-1538
 
1.2%
2020-12-2338
 
1.2%
Other values (102)861
27.9%
(Missing)1699
55.1%

Length

2022-09-17T13:29:09.705009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-12-1861
 
4.4%
2020-12-3061
 
4.4%
2020-12-1159
 
4.3%
2021-01-0758
 
4.2%
2020-12-1657
 
4.1%
2021-01-0552
 
3.8%
2020-12-2249
 
3.5%
2020-12-2449
 
3.5%
2020-12-1538
 
2.7%
2020-12-2338
 
2.7%
Other values (102)861
62.3%

Most occurring characters

ValueCountFrequency (%)
24186
30.3%
03292
23.8%
-2766
20.0%
12416
17.5%
5212
 
1.5%
3196
 
1.4%
4182
 
1.3%
8172
 
1.2%
7164
 
1.2%
6136
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number11064
80.0%
Dash Punctuation2766
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
24186
37.8%
03292
29.8%
12416
21.8%
5212
 
1.9%
3196
 
1.8%
4182
 
1.6%
8172
 
1.6%
7164
 
1.5%
6136
 
1.2%
9108
 
1.0%
Dash Punctuation
ValueCountFrequency (%)
-2766
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common13830
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
24186
30.3%
03292
23.8%
-2766
20.0%
12416
17.5%
5212
 
1.5%
3196
 
1.4%
4182
 
1.3%
8172
 
1.2%
7164
 
1.2%
6136
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII13830
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24186
30.3%
03292
23.8%
-2766
20.0%
12416
17.5%
5212
 
1.5%
3196
 
1.4%
4182
 
1.3%
8172
 
1.2%
7164
 
1.2%
6136
 
1.0%

_embedded.show.dvdCountry.timezone
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)18.5%
Missing3055
Missing (%)99.1%
Memory size24.2 KiB
Europe/Zaporozhye
10 
Asia/Seoul
8 
Asia/Tokyo
4 
Asia/Kamchatka
4 
Europe/Warsaw
 
1

Length

Max length17
Median length14
Mean length13.2962963
Min length10

Characters and Unicode

Total characters359
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.7%

Sample

1st rowAsia/Tokyo
2nd rowEurope/Zaporozhye
3rd rowAsia/Seoul
4th rowAsia/Kamchatka
5th rowEurope/Zaporozhye

Common Values

ValueCountFrequency (%)
Europe/Zaporozhye10
 
0.3%
Asia/Seoul8
 
0.3%
Asia/Tokyo4
 
0.1%
Asia/Kamchatka4
 
0.1%
Europe/Warsaw1
 
< 0.1%
(Missing)3055
99.1%

Length

2022-09-17T13:29:09.899259image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-17T13:29:10.152911image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
europe/zaporozhye10
37.0%
asia/seoul8
29.6%
asia/tokyo4
 
14.8%
asia/kamchatka4
 
14.8%
europe/warsaw1
 
3.7%

Most occurring characters

ValueCountFrequency (%)
o47
13.1%
a40
 
11.1%
e29
 
8.1%
/27
 
7.5%
r22
 
6.1%
p21
 
5.8%
u19
 
5.3%
s17
 
4.7%
A16
 
4.5%
i16
 
4.5%
Other values (15)105
29.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter278
77.4%
Uppercase Letter54
 
15.0%
Other Punctuation27
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o47
16.9%
a40
14.4%
e29
10.4%
r22
7.9%
p21
7.6%
u19
6.8%
s17
 
6.1%
i16
 
5.8%
h14
 
5.0%
y14
 
5.0%
Other values (7)39
14.0%
Uppercase Letter
ValueCountFrequency (%)
A16
29.6%
E11
20.4%
Z10
18.5%
S8
14.8%
T4
 
7.4%
K4
 
7.4%
W1
 
1.9%
Other Punctuation
ValueCountFrequency (%)
/27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin332
92.5%
Common27
 
7.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o47
14.2%
a40
12.0%
e29
 
8.7%
r22
 
6.6%
p21
 
6.3%
u19
 
5.7%
s17
 
5.1%
A16
 
4.8%
i16
 
4.8%
h14
 
4.2%
Other values (14)91
27.4%
Common
ValueCountFrequency (%)
/27
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII359
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o47
13.1%
a40
 
11.1%
e29
 
8.1%
/27
 
7.5%
r22
 
6.1%
p21
 
5.8%
u19
 
5.3%
s17
 
4.7%
A16
 
4.5%
i16
 
4.5%
Other values (15)105
29.2%

_embedded.show.dvdCountry.name
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)18.5%
Missing3055
Missing (%)99.1%
Memory size24.2 KiB
Ukraine
10 
Korea, Republic of
8 
Japan
4 
Russian Federation
4 
Poland
 
1

Length

Max length18
Median length7
Mean length11.55555556
Min length5

Characters and Unicode

Total characters312
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.7%

Sample

1st rowJapan
2nd rowUkraine
3rd rowKorea, Republic of
4th rowRussian Federation
5th rowUkraine

Common Values

ValueCountFrequency (%)
Ukraine10
 
0.3%
Korea, Republic of8
 
0.3%
Japan4
 
0.1%
Russian Federation4
 
0.1%
Poland1
 
< 0.1%
(Missing)3055
99.1%

Length

2022-09-17T13:29:10.358168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-17T13:29:10.570289image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
ukraine10
21.3%
korea8
17.0%
republic8
17.0%
of8
17.0%
japan4
 
8.5%
russian4
 
8.5%
federation4
 
8.5%
poland1
 
2.1%

Most occurring characters

ValueCountFrequency (%)
a35
 
11.2%
e34
 
10.9%
i26
 
8.3%
n23
 
7.4%
r22
 
7.1%
o21
 
6.7%
20
 
6.4%
p12
 
3.8%
R12
 
3.8%
u12
 
3.8%
Other values (14)95
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter245
78.5%
Uppercase Letter39
 
12.5%
Space Separator20
 
6.4%
Other Punctuation8
 
2.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a35
14.3%
e34
13.9%
i26
10.6%
n23
9.4%
r22
9.0%
o21
8.6%
p12
 
4.9%
u12
 
4.9%
k10
 
4.1%
l9
 
3.7%
Other values (6)41
16.7%
Uppercase Letter
ValueCountFrequency (%)
R12
30.8%
U10
25.6%
K8
20.5%
J4
 
10.3%
F4
 
10.3%
P1
 
2.6%
Space Separator
ValueCountFrequency (%)
20
100.0%
Other Punctuation
ValueCountFrequency (%)
,8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin284
91.0%
Common28
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a35
12.3%
e34
12.0%
i26
 
9.2%
n23
 
8.1%
r22
 
7.7%
o21
 
7.4%
p12
 
4.2%
R12
 
4.2%
u12
 
4.2%
k10
 
3.5%
Other values (12)77
27.1%
Common
ValueCountFrequency (%)
20
71.4%
,8
 
28.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII312
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a35
 
11.2%
e34
 
10.9%
i26
 
8.3%
n23
 
7.4%
r22
 
7.1%
o21
 
6.7%
20
 
6.4%
p12
 
3.8%
R12
 
3.8%
u12
 
3.8%
Other values (14)95
30.4%

_embedded.show.dvdCountry.code
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)18.5%
Missing3055
Missing (%)99.1%
Memory size24.2 KiB
UA
10 
KR
8 
JP
4 
RU
4 
PL
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters54
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)3.7%

Sample

1st rowJP
2nd rowUA
3rd rowKR
4th rowRU
5th rowUA

Common Values

ValueCountFrequency (%)
UA10
 
0.3%
KR8
 
0.3%
JP4
 
0.1%
RU4
 
0.1%
PL1
 
< 0.1%
(Missing)3055
99.1%

Length

2022-09-17T13:29:10.750781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-17T13:29:10.944920image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
ua10
37.0%
kr8
29.6%
jp4
 
14.8%
ru4
 
14.8%
pl1
 
3.7%

Most occurring characters

ValueCountFrequency (%)
U14
25.9%
R12
22.2%
A10
18.5%
K8
14.8%
P5
 
9.3%
J4
 
7.4%
L1
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter54
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U14
25.9%
R12
22.2%
A10
18.5%
K8
14.8%
P5
 
9.3%
J4
 
7.4%
L1
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Latin54
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U14
25.9%
R12
22.2%
A10
18.5%
K8
14.8%
P5
 
9.3%
J4
 
7.4%
L1
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII54
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U14
25.9%
R12
22.2%
A10
18.5%
K8
14.8%
P5
 
9.3%
J4
 
7.4%
L1
 
1.9%

_embedded.show.dvdCountry
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing3082
Missing (%)100.0%
Memory size24.2 KiB

_embedded.show.averageRuntime
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct90
Distinct (%)3.1%
Missing169
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean36.8115345
Minimum1
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.2 KiB
2022-09-17T13:29:11.230079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q118
median30
Q345
95-th percentile91
Maximum300
Range299
Interquartile range (IQR)27

Descriptive statistics

Standard deviation28.10140055
Coefficient of variation (CV)0.7633857413
Kurtosis15.38266314
Mean36.8115345
Median Absolute Deviation (MAD)15
Skewness2.775943319
Sum107232
Variance789.6887131
MonotonicityNot monotonic
2022-09-17T13:29:11.526697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45465
 
15.1%
30175
 
5.7%
60129
 
4.2%
20124
 
4.0%
25116
 
3.8%
12107
 
3.5%
5086
 
2.8%
1584
 
2.7%
12070
 
2.3%
566
 
2.1%
Other values (80)1491
48.4%
(Missing)169
 
5.5%
ValueCountFrequency (%)
14
 
0.1%
215
 
0.5%
36
 
0.2%
420
 
0.6%
566
2.1%
617
 
0.6%
752
1.7%
846
1.5%
938
1.2%
1060
1.9%
ValueCountFrequency (%)
3004
0.1%
2123
0.1%
1942
0.1%
1931
 
< 0.1%
1883
0.1%
1814
0.1%
1802
0.1%
1351
 
< 0.1%
1304
0.1%
1292
0.1%

_embedded.show._links.self.href
Categorical

HIGH CARDINALITY

Distinct633
Distinct (%)20.5%
Missing0
Missing (%)0.0%
Memory size24.2 KiB
https://api.tvmaze.com/shows/15250
 
38
https://api.tvmaze.com/shows/55355
 
32
https://api.tvmaze.com/shows/30606
 
31
https://api.tvmaze.com/shows/52743
 
30
https://api.tvmaze.com/shows/52104
 
29
Other values (628)
2922 

Length

Max length34
Median length34
Mean length33.97177158
Min length32

Characters and Unicode

Total characters104701
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique122 ?
Unique (%)4.0%

Sample

1st rowhttps://api.tvmaze.com/shows/41648
2nd rowhttps://api.tvmaze.com/shows/52198
3rd rowhttps://api.tvmaze.com/shows/52933
4th rowhttps://api.tvmaze.com/shows/51336
5th rowhttps://api.tvmaze.com/shows/54033

Common Values

ValueCountFrequency (%)
https://api.tvmaze.com/shows/1525038
 
1.2%
https://api.tvmaze.com/shows/5535532
 
1.0%
https://api.tvmaze.com/shows/3060631
 
1.0%
https://api.tvmaze.com/shows/5274330
 
1.0%
https://api.tvmaze.com/shows/5210429
 
0.9%
https://api.tvmaze.com/shows/5242128
 
0.9%
https://api.tvmaze.com/shows/4791226
 
0.8%
https://api.tvmaze.com/shows/5280626
 
0.8%
https://api.tvmaze.com/shows/5252424
 
0.8%
https://api.tvmaze.com/shows/5265524
 
0.8%
Other values (623)2794
90.7%

Length

2022-09-17T13:29:11.825269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://api.tvmaze.com/shows/1525038
 
1.2%
https://api.tvmaze.com/shows/5535532
 
1.0%
https://api.tvmaze.com/shows/3060631
 
1.0%
https://api.tvmaze.com/shows/5274330
 
1.0%
https://api.tvmaze.com/shows/5210429
 
0.9%
https://api.tvmaze.com/shows/5242128
 
0.9%
https://api.tvmaze.com/shows/4791226
 
0.8%
https://api.tvmaze.com/shows/5280626
 
0.8%
https://api.tvmaze.com/shows/5252424
 
0.8%
https://api.tvmaze.com/shows/5265524
 
0.8%
Other values (623)2794
90.7%

Most occurring characters

ValueCountFrequency (%)
/12328
 
11.8%
s9246
 
8.8%
t9246
 
8.8%
h6164
 
5.9%
p6164
 
5.9%
a6164
 
5.9%
o6164
 
5.9%
.6164
 
5.9%
m6164
 
5.9%
e3082
 
2.9%
Other values (16)33815
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter67804
64.8%
Other Punctuation21574
 
20.6%
Decimal Number15323
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s9246
13.6%
t9246
13.6%
h6164
9.1%
p6164
9.1%
a6164
9.1%
o6164
9.1%
m6164
9.1%
e3082
 
4.5%
w3082
 
4.5%
c3082
 
4.5%
Other values (3)9246
13.6%
Decimal Number
ValueCountFrequency (%)
52953
19.3%
41866
12.2%
21856
12.1%
11544
10.1%
61327
8.7%
31278
8.3%
01238
8.1%
91138
 
7.4%
81071
 
7.0%
71052
 
6.9%
Other Punctuation
ValueCountFrequency (%)
/12328
57.1%
.6164
28.6%
:3082
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin67804
64.8%
Common36897
35.2%

Most frequent character per script

Common
ValueCountFrequency (%)
/12328
33.4%
.6164
16.7%
:3082
 
8.4%
52953
 
8.0%
41866
 
5.1%
21856
 
5.0%
11544
 
4.2%
61327
 
3.6%
31278
 
3.5%
01238
 
3.4%
Other values (3)3261
 
8.8%
Latin
ValueCountFrequency (%)
s9246
13.6%
t9246
13.6%
h6164
9.1%
p6164
9.1%
a6164
9.1%
o6164
9.1%
m6164
9.1%
e3082
 
4.5%
w3082
 
4.5%
c3082
 
4.5%
Other values (3)9246
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII104701
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/12328
 
11.8%
s9246
 
8.8%
t9246
 
8.8%
h6164
 
5.9%
p6164
 
5.9%
a6164
 
5.9%
o6164
 
5.9%
.6164
 
5.9%
m6164
 
5.9%
e3082
 
2.9%
Other values (16)33815
32.3%

Interactions

2022-09-17T13:28:52.478581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:22.587292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:25.361392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:28.088638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:30.873998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:33.539879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:36.368673image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:38.976874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:41.475209image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:43.923520image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:46.465216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:48.973267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:52.733059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:22.813101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:25.606304image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:28.359444image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:31.140034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:33.766283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:36.568970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:39.188493image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:41.738115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:44.144633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:46.660620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:49.702703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:52.960125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:23.058686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:25.816635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:28.570616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:31.374655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:33.977135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:36.779516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:39.395127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:41.936324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:44.356516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:46.838659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:49.934631image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:53.324075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:23.270755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:26.008377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:28.766054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:31.595382image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:34.205228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:36.975842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:39.594177image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:42.119905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:44.556896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:47.025035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:50.156870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:53.690550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:23.464156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:26.214373image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:28.972835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:31.804469image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:34.411310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:37.178166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:39.788413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:42.300709image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:44.776313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:47.218324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:50.493484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:53.972157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:23.682930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:26.425462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:29.239445image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:32.051484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:34.629542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:37.393494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:40.014346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:42.503822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:44.989408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:47.446656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:50.758147image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:54.209232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:24.179851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:26.645365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:29.440778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:32.258099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:34.829470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:37.606698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:40.195149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:42.720942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:45.203223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:47.661581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:50.978333image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:54.482479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:24.353244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:26.977743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:29.639303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:32.450652image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:35.021159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:37.776079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:40.395583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:42.948826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:45.392897image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:47.901500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:51.218153image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:54.797753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:24.559226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:27.236407image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:29.975917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:32.663051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:35.214955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:37.966983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:40.592425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:43.147693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:45.592336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:48.104934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:51.477916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:55.069417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:24.767769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:27.448575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:30.242033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:32.888824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:35.787328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:38.200840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:40.800776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:43.345064image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:45.810475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:48.285348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:51.727601image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:55.277383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:24.923651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:27.604914image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:30.429110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:33.071629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:35.944150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:38.495733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:41.002092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:43.535070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:45.992558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:48.556879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:51.933642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:55.605449image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:25.155183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:27.862355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:30.646378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:33.331110image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:36.154604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:38.739029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:41.219486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:43.725939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:46.226781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:48.760581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-09-17T13:28:52.206806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-09-17T13:29:12.092902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-17T13:29:12.578000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-17T13:29:13.064947image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-17T13:29:13.421748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-17T13:29:13.745279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-17T13:28:56.198636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-17T13:28:58.384745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-17T13:28:59.171790image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-17T13:28:59.928576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

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0041648https://www.tvmaze.com/shows/41648/sim-for-youSim for YouRealityKorean[]Running16.016.02019-03-25Nonehttps://www.vlive.tv/video/121637[Monday, Wednesday, Friday]NaN35NaNNaNNaNNaNNaN361541.0NoneNaNNaNhttps://static.tvmaze.com/uploads/images/original_untouched/190/476668.jpg<p><b>Sim for You</b> is a reality series that chronicles each EXO member's life and reveal stories from their everyday life as a series.</p>1608499007https://api.tvmaze.com/shows/41648122.0NaN1979824
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2252933https://www.tvmaze.com/shows/52933/lab-s-antonom-belaevymLAB с Антоном БеляевымDocumentaryRussian[Music]To Be Determined26.025.02019-12-17Nonehttps://premier.one/show/lab-laboratoriya-muzyki-antona-belyaeva23:45[Saturday]NaN17NaNNaNNaNNaNNaNNaNNoneNaNhttps://static.tvmaze.com/uploads/images/medium_landscape/294/737206.jpghttps://static.tvmaze.com/uploads/images/original_untouched/379/948045.jpg<p>Russian music artists reveal themselves from unexpected sides in the Anton Belyaev's show.</p>1654035738https://api.tvmaze.com/shows/52933381.0308.02008027
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4454033https://www.tvmaze.com/shows/54033/wu-shen-zhu-zaiWu Shen Zhu ZaiAnimationChinese[Action, Adventure, Anime, Fantasy]Running8.08.02020-03-08Nonehttps://v.qq.com/detail/m/7q544xyrava3vxf.html10:00[Tuesday, Sunday]NaN80NaNNaNNaNNaNNaN379070.0NoneNaNNaNhttps://static.tvmaze.com/uploads/images/original_untouched/299/748854.jpg<p>The protagonist Qin Chen, who was originally the top genius in the military domain, was conspired by the people to fall into the death canyon in the forbidden land of the mainland. Qin Chen, who was inevitably dead, unexpectedly triggered the power of the mysterious ancient sword.<br /><br />Three hundred years later, in a remote part of the Tianwu mainland, a boy of the same name accidentally inherited Qin Chen's will. As the beloved grandson of King Dingwu of the Daqi National Army, due to the birth father's birth, the mother and son were treated coldly in Dingwu's palace and lived together. In order to rewrite the myth of the strong man in hope of the sun, and to protect everything he loves, Qin Chen resolutely took up the responsibility of maintaining the five kingdoms of the world and set foot on the road of martial arts again.</p>1649423444https://api.tvmaze.com/shows/54033104.0NaN2052503
5561674https://www.tvmaze.com/shows/61674/sono-koi-mousukoshi-atatamemasukaSono koi Mousukoshi AtatamemasukaScriptedJapanese[Romance]Ended15.015.02020-10-202020-12-22https://www.paravi.jp/static/koisuko22:00[Tuesday]NaN1NaNNaNNaNNaNNaN419045.0NoneNaNNaNhttps://static.tvmaze.com/uploads/images/original_untouched/404/1012331.jpg<p>It's spin-off drama of <b>"Kono Koi Atatamemasu ka"</b></p>1650915213https://api.tvmaze.com/shows/61674342.0NaN2315116
6663967https://www.tvmaze.com/shows/63967/run-btsRun BTS!VarietyKorean[]RunningNaNNaN2015-08-01NoneNone[Tuesday]NaN39NaNNaNNaNNaNNaN325091.0NoneNaNNaNhttps://static.tvmaze.com/uploads/images/original_untouched/421/1053306.jpg<p>A South Korean show by the boy group BTS. The show is all about BTS doing activities, challenges and lots more.</p>1662488262https://api.tvmaze.com/shows/63967346.0NaN2387985
7752038https://www.tvmaze.com/shows/52038/please-wait-brotherPlease Wait, BrotherScriptedChinese[Comedy]Ended37.037.02020-11-172020-12-08None12:00[Tuesday, Wednesday, Thursday]NaN19NaNNaNNaNNaNNaNNaNNoneNaNNaNhttps://static.tvmaze.com/uploads/images/original_untouched/284/711240.jpgNone1607697965https://api.tvmaze.com/shows/52038104.0NaN1973538
8852038https://www.tvmaze.com/shows/52038/please-wait-brotherPlease Wait, BrotherScriptedChinese[Comedy]Ended37.037.02020-11-172020-12-08None12:00[Tuesday, Wednesday, Thursday]NaN19NaNNaNNaNNaNNaNNaNNoneNaNNaNhttps://static.tvmaze.com/uploads/images/original_untouched/284/711240.jpgNone1607697965https://api.tvmaze.com/shows/52038104.0NaN1973539
9952373https://www.tvmaze.com/shows/52373/fearless-whispersFearless WhispersScriptedChinese[Drama, Romance, History]Ended60.060.02020-11-062020-12-01None[Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday]NaN26NaNNaNNaNNaNNaN391554.0NoneNaNNaNhttps://static.tvmaze.com/uploads/images/original_untouched/288/721078.jpg<p>A story revolving around a fresh graduate who holds an idealistic view of what's right and wrong, yet realizes that the very institution he chose to serve falls heavily onto a gray area caught in the struggles during chaotic times.</p>1607717005https://api.tvmaze.com/shows/52373NaN1282.01984264

Last rows

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307212259380https://www.tvmaze.com/shows/59380/forteresses-assiegees-batailles-de-legendeForteresses assiégées, batailles de légendeDocumentaryFrench[]Running51.052.02020-12-31Nonehttps://www.zed.fr/fr/tv/distribution/catalogue/programme/forteresses-assiegees-batailles-de-legende[Thursday]NaN1NaNNaNNaNNaNNaNNaNNoneNaNNaNNaN<p>Built at strategic points, and fitted with impressive fortifications and ingenious systems to counter attacks, fortresses are thought to be impenetrable. And yet, certain skillful warlords have successfully stormed them. How did they manage this?</p><p>By recounting how some of the most remarkable sieges - in ancient times or in medieval history - played out, this series revisits the construction of these megastructures and reveals the different strategies used to lay or to endure a siege. Thanks to CGI, dramatized scenes, and with the help of key experts, it immerses us in the compelling confrontation between the construction genius of the military strategists and the ingenuity of some exceptional warriors.</p>1639070620https://api.tvmaze.com/shows/59380188.0NaN2234297
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307512539053https://www.tvmaze.com/shows/39053/wwe-nxt-ukWWE NXT UKSportsEnglish[]Running60.060.02018-10-17NoneNone15:00[Thursday]NaN80NaNNaNNaNNaNNaN354295.0NoneNaNNaNhttps://static.tvmaze.com/uploads/images/original_untouched/401/1002870.jpg<p>The one-hour episodes will feature the biggest names from NXT UK, including Pete Dunne, Mark Andrews, Rhea Ripley, Toni Storm, Tyler Bate, Trent Seven and Wolfgang. Joining the NXT UK broadcasting team as backstage interviewer is British broadcasting personality Radzi Chinyanganya, best known for hosting ITV game show "Cannonball," and in his ongoing role as a presenter of the world's longest-running children's TV show, the BBC's "Blue Peter." Calling the action are commentators Nigel McGuinness and Vic Joseph, joined by ring announcer Andy Shepherd and NXT UK General Manager, the legendary Johnny Saint.</p>1661770465https://api.tvmaze.com/shows/3905315.0NaN1976649
307612639441https://www.tvmaze.com/shows/39441/carl-webers-the-family-businessCarl Weber's The Family BusinessScriptedEnglish[Drama, Crime, Thriller]Running60.059.02018-11-13Nonehttps://www.bet.plus/shows/carl-webers-the-family-business21:00[]5.095NaNNaNNaNNaNNaNNaNtt7146326NaNhttps://static.tvmaze.com/uploads/images/medium_landscape/292/731235.jpghttps://static.tvmaze.com/uploads/images/original_untouched/324/810377.jpg<p>Meet the Duncans, a prominent family from Jamaica, Queens living fast and luxurious. By day, they're an upstanding family; by night, they live a dangerous secret life. The patriarch of the family, L.C. Duncan, is at the age when he's starting to think about retirement and has to decide which of his children should take over his thriving exotic car dealership. The Duncans quickly come under siege from some of the top politicians, mafia and drug cartels in the city. The Duncans will have to stick together or die separately.</p>1662597506https://api.tvmaze.com/shows/39441351.0NaN2005096
307712739441https://www.tvmaze.com/shows/39441/carl-webers-the-family-businessCarl Weber's The Family BusinessScriptedEnglish[Drama, Crime, Thriller]Running60.059.02018-11-13Nonehttps://www.bet.plus/shows/carl-webers-the-family-business21:00[]5.095NaNNaNNaNNaNNaNNaNtt7146326NaNhttps://static.tvmaze.com/uploads/images/medium_landscape/292/731233.jpghttps://static.tvmaze.com/uploads/images/original_untouched/324/810377.jpg<p>Meet the Duncans, a prominent family from Jamaica, Queens living fast and luxurious. By day, they're an upstanding family; by night, they live a dangerous secret life. The patriarch of the family, L.C. Duncan, is at the age when he's starting to think about retirement and has to decide which of his children should take over his thriving exotic car dealership. The Duncans quickly come under siege from some of the top politicians, mafia and drug cartels in the city. The Duncans will have to stick together or die separately.</p>1662597506https://api.tvmaze.com/shows/39441351.0NaN2005098
307812839441https://www.tvmaze.com/shows/39441/carl-webers-the-family-businessCarl Weber's The Family BusinessScriptedEnglish[Drama, Crime, Thriller]Running60.059.02018-11-13Nonehttps://www.bet.plus/shows/carl-webers-the-family-business21:00[]5.095NaNNaNNaNNaNNaNNaNtt7146326NaNhttps://static.tvmaze.com/uploads/images/medium_landscape/292/732198.jpghttps://static.tvmaze.com/uploads/images/original_untouched/324/810377.jpg<p>Meet the Duncans, a prominent family from Jamaica, Queens living fast and luxurious. By day, they're an upstanding family; by night, they live a dangerous secret life. The patriarch of the family, L.C. Duncan, is at the age when he's starting to think about retirement and has to decide which of his children should take over his thriving exotic car dealership. The Duncans quickly come under siege from some of the top politicians, mafia and drug cartels in the city. The Duncans will have to stick together or die separately.</p>1662597506https://api.tvmaze.com/shows/39441351.0NaN2005099
307912939441https://www.tvmaze.com/shows/39441/carl-webers-the-family-businessCarl Weber's The Family BusinessScriptedEnglish[Drama, Crime, Thriller]Running60.059.02018-11-13Nonehttps://www.bet.plus/shows/carl-webers-the-family-business21:00[]5.095NaNNaNNaNNaNNaNNaNtt7146326NaNhttps://static.tvmaze.com/uploads/images/medium_landscape/292/732194.jpghttps://static.tvmaze.com/uploads/images/original_untouched/324/810377.jpg<p>Meet the Duncans, a prominent family from Jamaica, Queens living fast and luxurious. By day, they're an upstanding family; by night, they live a dangerous secret life. The patriarch of the family, L.C. Duncan, is at the age when he's starting to think about retirement and has to decide which of his children should take over his thriving exotic car dealership. The Duncans quickly come under siege from some of the top politicians, mafia and drug cartels in the city. The Duncans will have to stick together or die separately.</p>1662597506https://api.tvmaze.com/shows/39441351.0NaN2005100
308013039441https://www.tvmaze.com/shows/39441/carl-webers-the-family-businessCarl Weber's The Family BusinessScriptedEnglish[Drama, Crime, Thriller]Running60.059.02018-11-13Nonehttps://www.bet.plus/shows/carl-webers-the-family-business21:00[]5.095NaNNaNNaNNaNNaNNaNtt7146326NaNhttps://static.tvmaze.com/uploads/images/medium_landscape/292/732197.jpghttps://static.tvmaze.com/uploads/images/original_untouched/324/810377.jpg<p>Meet the Duncans, a prominent family from Jamaica, Queens living fast and luxurious. By day, they're an upstanding family; by night, they live a dangerous secret life. The patriarch of the family, L.C. Duncan, is at the age when he's starting to think about retirement and has to decide which of his children should take over his thriving exotic car dealership. The Duncans quickly come under siege from some of the top politicians, mafia and drug cartels in the city. The Duncans will have to stick together or die separately.</p>1662597506https://api.tvmaze.com/shows/39441351.0NaN2005101
308113139441https://www.tvmaze.com/shows/39441/carl-webers-the-family-businessCarl Weber's The Family BusinessScriptedEnglish[Drama, Crime, Thriller]Running60.059.02018-11-13Nonehttps://www.bet.plus/shows/carl-webers-the-family-business21:00[]5.095NaNNaNNaNNaNNaNNaNtt7146326NaNhttps://static.tvmaze.com/uploads/images/medium_landscape/292/731226.jpghttps://static.tvmaze.com/uploads/images/original_untouched/324/810377.jpg<p>Meet the Duncans, a prominent family from Jamaica, Queens living fast and luxurious. By day, they're an upstanding family; by night, they live a dangerous secret life. The patriarch of the family, L.C. Duncan, is at the age when he's starting to think about retirement and has to decide which of his children should take over his thriving exotic car dealership. The Duncans quickly come under siege from some of the top politicians, mafia and drug cartels in the city. The Duncans will have to stick together or die separately.</p>1662597506https://api.tvmaze.com/shows/39441351.0NaN2005102